The quantum hybrid algorithm has recently become a very promising and speedy method for solving larger-scale optimization problems in the noisy intermediate-scale quantum(NISQ)era.The unit commitment(UC)problem is a f...The quantum hybrid algorithm has recently become a very promising and speedy method for solving larger-scale optimization problems in the noisy intermediate-scale quantum(NISQ)era.The unit commitment(UC)problem is a fundamental problem in the field of power systems that aims to satisfy the power balance constraint with minimal cost.In this paper,we focus on the implementation of the UC solution using exact quantum algorithms based on the quantum neural network(QNN).This method is tested with a ten-unit system under the power balance constraint.In order to improve computing precision and reduce network complexity,we propose a knowledge-based partially connected quantum neural network(PCQNN).The results show that exact solutions can be obtained by the improved algorithm and that the depth of the quantum circuit can be reduced simultaneously.展开更多
Accurately forecasting peak particle velocity(PPV)during blasting operations plays a crucial role in mitigating vibration-related hazards and preventing economic losses.This research introduces an approach to PPV pred...Accurately forecasting peak particle velocity(PPV)during blasting operations plays a crucial role in mitigating vibration-related hazards and preventing economic losses.This research introduces an approach to PPV prediction by combining conventional empirical equations with physics-informed neural networks(PINN)and optimizing the model parameters via the Particle Swarm Optimization(PSO)algorithm.The proposed PSO-PINN framework was rigorously benchmarked against seven established machine learning approaches:Multilayer Perceptron(MLP),Extreme Gradient Boosting(XGBoost),Random Forest(RF),Support Vector Regression(SVR),Gradient Boosting Decision Tree(GBDT),Adaptive Boosting(Adaboost),and Gene Expression Programming(GEP).Comparative analysis showed that PSO-PINN outperformed these models,achieving RMSE reductions of 17.82-37.63%,MSE reductions of 32.47-61.10%,AR improvements of 2.97-21.19%,and R^(2)enhancements of 7.43-29.21%,demonstrating superior accuracy and generalization.Furthermore,the study determines the impact of incorporating empirical formulas as physical constraints in neural networks and examines the effects of different empirical equations,particle swarm size,iteration count in PSO,regularization coefficient,and learning rate in PINN on model performance.Lastly,a predictive system for blast vibration PPV is designed and implemented.The research outcomes offer theoretical references and practical recommendations for blast vibration forecasting in similar engineering applications.展开更多
Radial Basis Function Neural Network(RBFNN)ensembles have long suffered from non-efficient training,where incorrect parameter settings can be computationally disastrous.This paper examines different evolutionary algor...Radial Basis Function Neural Network(RBFNN)ensembles have long suffered from non-efficient training,where incorrect parameter settings can be computationally disastrous.This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network(SRBFNN)through the behavior’s integration of satisfiability programming.Inspired by evolutionary algorithms,which can iteratively find the nearoptimal solution,different Evolutionary Algorithms(EAs)were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation(SRBFNN-2SAT).The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms,including Genetic Algorithm(GA),Evolution Strategy Algorithm(ES),Differential Evolution Algorithm(DE),and Evolutionary Programming Algorithm(EP).Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming language.With the use of SRBFNN-2SAT,a training method based on these algorithms has been presented,then training has been compared among algorithms,which were applied in Microsoft Visual C++software using multiple metrics of performance,including Mean Absolute Relative Error(MARE),Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),Mean Bias Error(MBE),Systematic Error(SD),Schwarz Bayesian Criterion(SBC),and Central Process Unit time(CPU time).Based on the results,the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms.It has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight,accompanied by the slightest iteration error,which minimizes the objective function of SRBFNN-2SAT.展开更多
Feedforward Neural Network(FNN)is one of the most popular neural network models that is utilized to solve a wide range of nonlinear and complex problems.Several models such as stochastic gradient descent have been dev...Feedforward Neural Network(FNN)is one of the most popular neural network models that is utilized to solve a wide range of nonlinear and complex problems.Several models such as stochastic gradient descent have been developed to train FNNs.However,they mainly suffer from falling into local optima leading to reduce the accuracy of FNNs.Moreover,the convergence speed of training process depends on the initial values of weights and biases in FNNs.Generally,these values are randomly determined by most of the training models.To deal with these issues,in this paper,we develop a novel evolutionary algorithm by modifying the original version of Whale Optimization Algorithm(WOA).To this end,a nonlinear function is introduced to improve the exploration and exploitation phases in the search process of WOA.Then,the modified WOA is applied to automatically obtain the initial values of weights and biases in FNN leading to reduce the probability of falling into local optima.In addition,the FNN model trained by the modified WOA is used to develop a classification approach for medical diagnosis problems.Ten medical diagnosis datasets are utilized to evaluate the efficiency of the proposed method.Also,four evaluation metrics including accuracy,AUC,specificity,and sensitivity are used in the experiments to compare the performance of classification models.The experimental results demonstrate that the proposed method is better than other competing classification models due to achieving higher values of accuracy,AUC,specificity,and sensitivity metrics for the used datasets.展开更多
This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in boa...This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum.展开更多
Aim at the defects of easy to fall into the local minimum point and the low convergence speed of back propagation（BP）neural network in the gesture recognition, a new method that combines the chaos algorithm with the...Aim at the defects of easy to fall into the local minimum point and the low convergence speed of back propagation（BP）neural network in the gesture recognition, a new method that combines the chaos algorithm with the genetic algorithm（CGA） is proposed. According to the ergodicity of chaos algorithm and global convergence of genetic algorithm, the basic idea of this paper is to encode the weights and thresholds of BP neural network and obtain a general optimal solution with genetic algorithm, and then the general optimal solution is optimized to the accurate optimal solution by adding chaotic disturbance. The optimal results of the chaotic genetic algorithm are used as the initial weights and thresholds of the BP neural network to recognize the gesture. Simulation and experimental results show that the real-time performance and accuracy of the gesture recognition are greatly improved with CGA.展开更多
Fiber reinforced polymers (FRPs), unlike steel, are corrosion-resistant and therefore are of interest;however, their use is hindered because their brittle shear is formulated in most specifications using limited data ...Fiber reinforced polymers (FRPs), unlike steel, are corrosion-resistant and therefore are of interest;however, their use is hindered because their brittle shear is formulated in most specifications using limited data available at the time. We aimed to predict the shear strength of concrete beams reinforced with FRP bars and without stirrups by compiling a relatively large database of 198 previously published test results (available in appendix). To model shear strength, an artificial neural network was trained by an ensemble of Levenberg-Marquardt and imperialist competitive algorithms. The results suggested superior accuracy of model compared to equations available in specifications and literature.展开更多
This paper presents a seafloor classification method of multibeam sonar data, based on the use of Adaptive Resonance Theory （ART） neural networks. A general ART-based neural network, Fuzzy ARTMAP, has been proposed ...This paper presents a seafloor classification method of multibeam sonar data, based on the use of Adaptive Resonance Theory （ART） neural networks. A general ART-based neural network, Fuzzy ARTMAP, has been proposed for seafloor classification of multibeam sonar data. An evolutionary strategy was used to generate new training samples near the cluster boundaries of the neural network, therefore the weights can be revised and refined by supervised learning. The proposed method resolves the training problem for Fuzzy ARTMAP neural networks, which are applied to seafloor classification of multibeam sonar data when there are less than adequate ground-troth samples. The results were synthetically analyzed in comparison with the standard Fuzzy ARTMAP network and a conventional Bayesian classifier. The conclusion can be drawn that Fuzzy ARTMAP neural networks combining with GA algorithms can be alternative powerful tools for seafloor classification of multibeam sonar data.展开更多
Thermal deformation error is one of the most important factors affecting the CNCs’ accuracy, so research is conducted on the temperature errors affecting CNCs’ machining accuracy;on the basis of analyzing the unpred...Thermal deformation error is one of the most important factors affecting the CNCs’ accuracy, so research is conducted on the temperature errors affecting CNCs’ machining accuracy;on the basis of analyzing the unpredictability and pre-maturing of the results of the genetic algorithm, as well as the slow speed of the training speed of the particle algorithm, a kind of Mind Evolutionary Algorithm optimized BP neural network featuring extremely strong global search capacity was proposed;type KVC850MA/2 five-axis CNC of Changzheng Lathe Factory was used as the research subject, and the Mind Evolutionary Algorithm optimized BP neural network algorithm was used for the establishment of the compensation model between temperature changes and the CNCs’ thermal deformation errors, as well as the realization method on hardware. The simulation results indicated that this method featured extremely high practical value.展开更多
The traditional genetic algorithm(GA)has unstable inversion results and is easy to fall into the local optimum when inverting fault parameters.Therefore,this article considers the combination of GA with other non-line...The traditional genetic algorithm(GA)has unstable inversion results and is easy to fall into the local optimum when inverting fault parameters.Therefore,this article considers the combination of GA with other non-linear algorithms in order to improve the inversion precision of GA.This paper proposes a genetic Nelder-Mead neural network algorithm(GNMNNA).This algorithm uses a neural network algorithm(NNA)to optimize the global search ability of GA.At the same time,the simplex algorithm is used to optimize the local search capability of the GA.Through numerical examples,the stability of the inversion algorithm under different strategies is explored.The experimental results show that the proposed GNMNNA has stronger inversion stability and higher precision compared with the existing algorithms.The effectiveness of GNMNNA is verified by the BodrumeKos earthquake and Monte Cristo Range earthquake.The experimental results show that GNMNNA is superior to GA and NNA in both inversion precision and computational stability.Therefore,GNMNNA has greater application potential in complex earthquake environment.展开更多
Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomef...Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomefamous due to its high tendency towards convergence to the global optimummost of the time. But, still the standard bat with random walk has a problemof getting stuck in local minima. In order to solve this problem, this researchproposed bat algorithm with levy flight random walk. Then, the proposedBat with Levy flight algorithm is further hybridized with three differentvariants of ANN. The proposed BatLFBP is applied to the problem ofinsulin DNA sequence classification of healthy homosapien. For classificationperformance, the proposed models such as Bat levy flight Artificial NeuralNetwork (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) arecompared with the other state-of-the-art algorithms like Bat Artificial NeuralNetwork (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distributionback propagation (BatGDBP), in-terms of means squared error (MSE) andaccuracy. From the perspective of simulations results, it is show that theproposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185,and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on WL5.While on WL10 the proposed BatLFANN achieved 99.89899% accuracy withMSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853%accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracywith MSE of 0.006738 which achieve better accuracy as compared to the otherhybrid models.展开更多
A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluct...A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluctant, which led to the loss of valuable information and affected performance of the algorithm to certain extent. For multi layer feed forward neural networks, the second order back propagation recursive algorithm based generalized cost criteria was proposed. It is proved that it is equivalent to Newton recursive algorithm and has a second order convergent rate. The performance and application prospect are analyzed. Lots of simulation experiments indicate that the calculation of the new algorithm is almost equivalent to the recursive least square multiple algorithm. The algorithm and selection of networks parameters are significant and the performance is more excellent than BP algorithm and the second order learning algorithm that was given by Karayiannis.展开更多
This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers(VPLN)in challenging image datasets.Since binarization of the input ...This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers(VPLN)in challenging image datasets.Since binarization of the input image is the most important and difficult step in the detection of VPLN,a hybrid technique is introduced that fuses the outputs of three fast techniques into a pool of connected components objects(CCO)and hence enriches the solution space with more solution candidates.Due to the combination of the outputs of the three binarization techniques,many CCOs are produced into the output pool from which one or more sequences are to be selected as candidate solutions.The pool is filtered and submitted to a new memetic algorithm to select the best fit sequence of CCOs based on an objective distance between the tested sequence and the defined geometrical relationship matrix that represents the layout of the VPLN symbols inside the concerned plate prototype.Using any of the previous versions will give moderate results but with very low speed.Hence,a new local search is added as a memetic operator to increase the fitness of the best chromosomes based on the linear arrangement of the license plate symbols.The memetic operator speeds up the convergence to the best solution and hence compensates for the overhead of the used hybrid binarization techniques and allows for real-time detection especially after using GPUs in implementing most of the used techniques.Also,a deep convolutional network is used to detect false positives to prevent fake detection of non-plate text or similar patterns.Various image samples with a wide range of scale,orientation,and illumination conditions have been experimented with to verify the effect of the new improvements.Encouraging results with 97.55%detection precision have been reported using the recent challenging public Chinese City Parking Dataset(CCPD)outperforming the author of the dataset by 3.05%and the state-of-the-art technique by 1.45%.展开更多
The self-organization mapping （SOM） neural network algorithm is a new method used to identify the ecosystem service zones at regional extent. According to the ecosystem assessment framework of Millennium Ecosystem A...The self-organization mapping （SOM） neural network algorithm is a new method used to identify the ecosystem service zones at regional extent. According to the ecosystem assessment framework of Millennium Ecosystem Assessment （ MA）, this paper develops an indicator system and conducts a spatial cluster analysis at the 1km by I km grid pixel scale with the SOM neural network algorithm to sort the core ecosystem services over the vertical and horizontal dimensions. A case study was carried out in Xilingol League. The ecosystem services in Xilingol League could be divided to six different ecological zones. The SOM neural network algorithm was capable of identifying the similarities among the input data automatically. The research provides both spatially and temporally valuable information targeted sustainable ecosystem management for decision-makers.展开更多
A novel real coded improved genetic algorithm (GA) of training feed forward neural network is proposed to realize nonlinear system forecast. The improved GA employs a generation alternation model based the minimal gen...A novel real coded improved genetic algorithm (GA) of training feed forward neural network is proposed to realize nonlinear system forecast. The improved GA employs a generation alternation model based the minimal generation gap (MGP) and blend crossover operators (BLX α). Compared with traditional GA implemented in binary number, the processing time of the improved GA is faster because coding and decoding are unnecessary. In addition, it needn t set parameters such as the probability value of crossove...展开更多
Accurate prediction of chemical composition of vacuum gas oil （VGO） is essential for the routine operation of refineries. In this work, a new approach for auto-design of artificial neural networks （ANN） based on a...Accurate prediction of chemical composition of vacuum gas oil （VGO） is essential for the routine operation of refineries. In this work, a new approach for auto-design of artificial neural networks （ANN） based on a genetic algorithm （GA） is developed for predicting VGO saturates. The number of neurons in the hidden layer, the momentum and the learning rates are determined by using the genetic algorithm. The inputs for the artificial neural networks model are five physical properties, namely, average boiling point, density, molecular weight, viscosity and refractive index. It is verified that the genetic algorithm could find the optimal structural parameters and training parameters of ANN. In addition, an artificial neural networks model based on a genetic algorithm was tested and the results indicated that the VGO saturates can be efficiently predicted. Compared with conventional artificial neural networks models, this approach can improve the prediction accuracy.展开更多
Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the bes...Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.展开更多
In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. ...In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. By the learning ability of the NN and the closely approximate unknown function to any degree of desired accuracy,the input-output mapping relationship between coordinates and the measurement data of time of arrival （TOA） and time difference of arrival （TDOA） is established. A real-time learning algorithm based on the extended Kalman filter （EKF） is used to train the multilayer perceptron （MLP） network by treating the linkweights of a network as the states of the nonlinear dynamic system. Since the EKF-based learning algorithm approximately gives the minimum variance estimate of the linkweights,the convergence is improved in comparison with the backwards error propagation （BP） algorithm. Numerical results illustrate thatthe proposedalgorithmcanachieve enhanced accuracy,and the performance ofthe algorithmis betterthanthat of the BP-based NN algorithm and the least squares （LS） algorithm in the NLOS environments. Moreover,this location method does not depend on a particular distribution of the NLOS error and does not need line-of-sight （ LOS ） or NLOS identification.展开更多
For optimal design of mechanical clinching steel-aluminum joints,the back propagation(BP)neural network is used to research the mapping relationship between joining technique parameters including sheet thickness,sheet...For optimal design of mechanical clinching steel-aluminum joints,the back propagation(BP)neural network is used to research the mapping relationship between joining technique parameters including sheet thickness,sheet hardness,joint bottom diameter etc.,and mechanical properties of shearing and peeling in order to investigate joining technology between various material plates in the steel-aluminum hybrid structure car body.Genetic algorithm(GA)is adopted to optimize the back-propagation neural network connection weights.The training and validating samples are made by the BTM Tog-L-Loc system with different technologic parameters.The training samples'parameters and the corresponding joints'mechanical properties are supplied to the artificial neural network(ANN)for training.The validating samples'experimental data is used for checking up the prediction outputs.The calculation results show that GA can improve the model's prediction precision and generalization ability of BP neural network.The comparative analysis between the experimental data and the prediction outputs shows that ANN prediction models after training can effectively predict the mechanical properties of mechanical clinching joints and prove the feasibility and reliability of the intelligent neural networks system when used in the mechanical properties prediction of mechanical clinching joints.The prediction results can be used for a reference in the design of mechanical clinching steel-aluminum joints.展开更多
While various kinds of fibers are used to improve the hot mix asphalt(HMA) performance, a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, the fatigue life of modified HMA samples using po...While various kinds of fibers are used to improve the hot mix asphalt(HMA) performance, a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, the fatigue life of modified HMA samples using polypropylene and polyester fibers was evaluated and two models namely regression and artificial neural network(ANN) were used to predict the fatigue life based on the fibers parameters. As ANN contains many parameters such as the number of hidden layers which directly influence the prediction accuracy, genetic algorithm(GA) was used to solve optimization problem for ANN. Moreover, the trial and error method was used to optimize the GA parameters such as the population size. The comparison of the results obtained from regression and optimized ANN with GA shows that the two-hidden-layer ANN with two and five neurons in the first and second hidden layers, respectively, can predict the fatigue life of fiber-reinforced HMA with high accuracy(correlation coefficient of 0.96).展开更多
基金supported in part by the China Postdoctoral Science Foundation(Grant No.2023M740874)。
文摘The quantum hybrid algorithm has recently become a very promising and speedy method for solving larger-scale optimization problems in the noisy intermediate-scale quantum(NISQ)era.The unit commitment(UC)problem is a fundamental problem in the field of power systems that aims to satisfy the power balance constraint with minimal cost.In this paper,we focus on the implementation of the UC solution using exact quantum algorithms based on the quantum neural network(QNN).This method is tested with a ten-unit system under the power balance constraint.In order to improve computing precision and reduce network complexity,we propose a knowledge-based partially connected quantum neural network(PCQNN).The results show that exact solutions can be obtained by the improved algorithm and that the depth of the quantum circuit can be reduced simultaneously.
基金supported by the National Natural Science Foundation of China(Grant No.52409143)the Basic Scientific Research Fund of Changjiang River Scientific Research Institute for Central-level Public Welfare Research Institutes(Grant No.CKSF2025184/YT)the Hubei Provincial Natural Science Foundation of China(Grant No.2022CFB673).
文摘Accurately forecasting peak particle velocity(PPV)during blasting operations plays a crucial role in mitigating vibration-related hazards and preventing economic losses.This research introduces an approach to PPV prediction by combining conventional empirical equations with physics-informed neural networks(PINN)and optimizing the model parameters via the Particle Swarm Optimization(PSO)algorithm.The proposed PSO-PINN framework was rigorously benchmarked against seven established machine learning approaches:Multilayer Perceptron(MLP),Extreme Gradient Boosting(XGBoost),Random Forest(RF),Support Vector Regression(SVR),Gradient Boosting Decision Tree(GBDT),Adaptive Boosting(Adaboost),and Gene Expression Programming(GEP).Comparative analysis showed that PSO-PINN outperformed these models,achieving RMSE reductions of 17.82-37.63%,MSE reductions of 32.47-61.10%,AR improvements of 2.97-21.19%,and R^(2)enhancements of 7.43-29.21%,demonstrating superior accuracy and generalization.Furthermore,the study determines the impact of incorporating empirical formulas as physical constraints in neural networks and examines the effects of different empirical equations,particle swarm size,iteration count in PSO,regularization coefficient,and learning rate in PINN on model performance.Lastly,a predictive system for blast vibration PPV is designed and implemented.The research outcomes offer theoretical references and practical recommendations for blast vibration forecasting in similar engineering applications.
基金This work is supported by Ministry of Higher Education(MOHE)through Fundamental Research Grant Scheme(FRGS)(FRGS/1/2020/STG06/UTHM/03/7).
文摘Radial Basis Function Neural Network(RBFNN)ensembles have long suffered from non-efficient training,where incorrect parameter settings can be computationally disastrous.This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network(SRBFNN)through the behavior’s integration of satisfiability programming.Inspired by evolutionary algorithms,which can iteratively find the nearoptimal solution,different Evolutionary Algorithms(EAs)were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation(SRBFNN-2SAT).The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms,including Genetic Algorithm(GA),Evolution Strategy Algorithm(ES),Differential Evolution Algorithm(DE),and Evolutionary Programming Algorithm(EP).Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming language.With the use of SRBFNN-2SAT,a training method based on these algorithms has been presented,then training has been compared among algorithms,which were applied in Microsoft Visual C++software using multiple metrics of performance,including Mean Absolute Relative Error(MARE),Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),Mean Bias Error(MBE),Systematic Error(SD),Schwarz Bayesian Criterion(SBC),and Central Process Unit time(CPU time).Based on the results,the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms.It has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight,accompanied by the slightest iteration error,which minimizes the objective function of SRBFNN-2SAT.
文摘Feedforward Neural Network(FNN)is one of the most popular neural network models that is utilized to solve a wide range of nonlinear and complex problems.Several models such as stochastic gradient descent have been developed to train FNNs.However,they mainly suffer from falling into local optima leading to reduce the accuracy of FNNs.Moreover,the convergence speed of training process depends on the initial values of weights and biases in FNNs.Generally,these values are randomly determined by most of the training models.To deal with these issues,in this paper,we develop a novel evolutionary algorithm by modifying the original version of Whale Optimization Algorithm(WOA).To this end,a nonlinear function is introduced to improve the exploration and exploitation phases in the search process of WOA.Then,the modified WOA is applied to automatically obtain the initial values of weights and biases in FNN leading to reduce the probability of falling into local optima.In addition,the FNN model trained by the modified WOA is used to develop a classification approach for medical diagnosis problems.Ten medical diagnosis datasets are utilized to evaluate the efficiency of the proposed method.Also,four evaluation metrics including accuracy,AUC,specificity,and sensitivity are used in the experiments to compare the performance of classification models.The experimental results demonstrate that the proposed method is better than other competing classification models due to achieving higher values of accuracy,AUC,specificity,and sensitivity metrics for the used datasets.
基金This paper is supported by the Nature Science Foundation of Heilongjiang Province.
文摘This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum.
基金supported by Natural Science Foundation of Heilongjiang Province Youth Fund(No.QC2014C054)Foundation for University Young Key Scholar by Heilongjiang Province(No.1254G023)the Science Funds for the Young Innovative Talents of HUST(No.201304)
文摘Aim at the defects of easy to fall into the local minimum point and the low convergence speed of back propagation（BP）neural network in the gesture recognition, a new method that combines the chaos algorithm with the genetic algorithm（CGA） is proposed. According to the ergodicity of chaos algorithm and global convergence of genetic algorithm, the basic idea of this paper is to encode the weights and thresholds of BP neural network and obtain a general optimal solution with genetic algorithm, and then the general optimal solution is optimized to the accurate optimal solution by adding chaotic disturbance. The optimal results of the chaotic genetic algorithm are used as the initial weights and thresholds of the BP neural network to recognize the gesture. Simulation and experimental results show that the real-time performance and accuracy of the gesture recognition are greatly improved with CGA.
文摘Fiber reinforced polymers (FRPs), unlike steel, are corrosion-resistant and therefore are of interest;however, their use is hindered because their brittle shear is formulated in most specifications using limited data available at the time. We aimed to predict the shear strength of concrete beams reinforced with FRP bars and without stirrups by compiling a relatively large database of 198 previously published test results (available in appendix). To model shear strength, an artificial neural network was trained by an ensemble of Levenberg-Marquardt and imperialist competitive algorithms. The results suggested superior accuracy of model compared to equations available in specifications and literature.
文摘This paper presents a seafloor classification method of multibeam sonar data, based on the use of Adaptive Resonance Theory （ART） neural networks. A general ART-based neural network, Fuzzy ARTMAP, has been proposed for seafloor classification of multibeam sonar data. An evolutionary strategy was used to generate new training samples near the cluster boundaries of the neural network, therefore the weights can be revised and refined by supervised learning. The proposed method resolves the training problem for Fuzzy ARTMAP neural networks, which are applied to seafloor classification of multibeam sonar data when there are less than adequate ground-troth samples. The results were synthetically analyzed in comparison with the standard Fuzzy ARTMAP network and a conventional Bayesian classifier. The conclusion can be drawn that Fuzzy ARTMAP neural networks combining with GA algorithms can be alternative powerful tools for seafloor classification of multibeam sonar data.
文摘Thermal deformation error is one of the most important factors affecting the CNCs’ accuracy, so research is conducted on the temperature errors affecting CNCs’ machining accuracy;on the basis of analyzing the unpredictability and pre-maturing of the results of the genetic algorithm, as well as the slow speed of the training speed of the particle algorithm, a kind of Mind Evolutionary Algorithm optimized BP neural network featuring extremely strong global search capacity was proposed;type KVC850MA/2 five-axis CNC of Changzheng Lathe Factory was used as the research subject, and the Mind Evolutionary Algorithm optimized BP neural network algorithm was used for the establishment of the compensation model between temperature changes and the CNCs’ thermal deformation errors, as well as the realization method on hardware. The simulation results indicated that this method featured extremely high practical value.
基金This manuscript is supported by the National Natural Science Foundation of China(No.42174011,41874001 and 42174011).
文摘The traditional genetic algorithm(GA)has unstable inversion results and is easy to fall into the local optimum when inverting fault parameters.Therefore,this article considers the combination of GA with other non-linear algorithms in order to improve the inversion precision of GA.This paper proposes a genetic Nelder-Mead neural network algorithm(GNMNNA).This algorithm uses a neural network algorithm(NNA)to optimize the global search ability of GA.At the same time,the simplex algorithm is used to optimize the local search capability of the GA.Through numerical examples,the stability of the inversion algorithm under different strategies is explored.The experimental results show that the proposed GNMNNA has stronger inversion stability and higher precision compared with the existing algorithms.The effectiveness of GNMNNA is verified by the BodrumeKos earthquake and Monte Cristo Range earthquake.The experimental results show that GNMNNA is superior to GA and NNA in both inversion precision and computational stability.Therefore,GNMNNA has greater application potential in complex earthquake environment.
基金This research is supported by Tier-1 Research Grant, vote no. H938 by ResearchManagement Office (RMC), Universiti Tun Hussein Onn Malaysia and Ministry of Higher Education,Malaysia.
文摘Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomefamous due to its high tendency towards convergence to the global optimummost of the time. But, still the standard bat with random walk has a problemof getting stuck in local minima. In order to solve this problem, this researchproposed bat algorithm with levy flight random walk. Then, the proposedBat with Levy flight algorithm is further hybridized with three differentvariants of ANN. The proposed BatLFBP is applied to the problem ofinsulin DNA sequence classification of healthy homosapien. For classificationperformance, the proposed models such as Bat levy flight Artificial NeuralNetwork (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) arecompared with the other state-of-the-art algorithms like Bat Artificial NeuralNetwork (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distributionback propagation (BatGDBP), in-terms of means squared error (MSE) andaccuracy. From the perspective of simulations results, it is show that theproposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185,and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on WL5.While on WL10 the proposed BatLFANN achieved 99.89899% accuracy withMSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853%accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracywith MSE of 0.006738 which achieve better accuracy as compared to the otherhybrid models.
文摘A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluctant, which led to the loss of valuable information and affected performance of the algorithm to certain extent. For multi layer feed forward neural networks, the second order back propagation recursive algorithm based generalized cost criteria was proposed. It is proved that it is equivalent to Newton recursive algorithm and has a second order convergent rate. The performance and application prospect are analyzed. Lots of simulation experiments indicate that the calculation of the new algorithm is almost equivalent to the recursive least square multiple algorithm. The algorithm and selection of networks parameters are significant and the performance is more excellent than BP algorithm and the second order learning algorithm that was given by Karayiannis.
文摘This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers(VPLN)in challenging image datasets.Since binarization of the input image is the most important and difficult step in the detection of VPLN,a hybrid technique is introduced that fuses the outputs of three fast techniques into a pool of connected components objects(CCO)and hence enriches the solution space with more solution candidates.Due to the combination of the outputs of the three binarization techniques,many CCOs are produced into the output pool from which one or more sequences are to be selected as candidate solutions.The pool is filtered and submitted to a new memetic algorithm to select the best fit sequence of CCOs based on an objective distance between the tested sequence and the defined geometrical relationship matrix that represents the layout of the VPLN symbols inside the concerned plate prototype.Using any of the previous versions will give moderate results but with very low speed.Hence,a new local search is added as a memetic operator to increase the fitness of the best chromosomes based on the linear arrangement of the license plate symbols.The memetic operator speeds up the convergence to the best solution and hence compensates for the overhead of the used hybrid binarization techniques and allows for real-time detection especially after using GPUs in implementing most of the used techniques.Also,a deep convolutional network is used to detect false positives to prevent fake detection of non-plate text or similar patterns.Various image samples with a wide range of scale,orientation,and illumination conditions have been experimented with to verify the effect of the new improvements.Encouraging results with 97.55%detection precision have been reported using the recent challenging public Chinese City Parking Dataset(CCPD)outperforming the author of the dataset by 3.05%and the state-of-the-art technique by 1.45%.
基金Supported by the National Scientific Foundation of China(4080123170873118)+6 种基金the Chinese Academy of Sciences(KZCX2-YW-305-2KSCX2-YW-N-039KZCX2-YW-326-1)the Ministry of Science and Technology of China(2006DFB91912012006BAC08B032006BAC08B062008BAK47B02)~~
文摘The self-organization mapping （SOM） neural network algorithm is a new method used to identify the ecosystem service zones at regional extent. According to the ecosystem assessment framework of Millennium Ecosystem Assessment （ MA）, this paper develops an indicator system and conducts a spatial cluster analysis at the 1km by I km grid pixel scale with the SOM neural network algorithm to sort the core ecosystem services over the vertical and horizontal dimensions. A case study was carried out in Xilingol League. The ecosystem services in Xilingol League could be divided to six different ecological zones. The SOM neural network algorithm was capable of identifying the similarities among the input data automatically. The research provides both spatially and temporally valuable information targeted sustainable ecosystem management for decision-makers.
文摘A novel real coded improved genetic algorithm (GA) of training feed forward neural network is proposed to realize nonlinear system forecast. The improved GA employs a generation alternation model based the minimal generation gap (MGP) and blend crossover operators (BLX α). Compared with traditional GA implemented in binary number, the processing time of the improved GA is faster because coding and decoding are unnecessary. In addition, it needn t set parameters such as the probability value of crossove...
文摘Accurate prediction of chemical composition of vacuum gas oil （VGO） is essential for the routine operation of refineries. In this work, a new approach for auto-design of artificial neural networks （ANN） based on a genetic algorithm （GA） is developed for predicting VGO saturates. The number of neurons in the hidden layer, the momentum and the learning rates are determined by using the genetic algorithm. The inputs for the artificial neural networks model are five physical properties, namely, average boiling point, density, molecular weight, viscosity and refractive index. It is verified that the genetic algorithm could find the optimal structural parameters and training parameters of ANN. In addition, an artificial neural networks model based on a genetic algorithm was tested and the results indicated that the VGO saturates can be efficiently predicted. Compared with conventional artificial neural networks models, this approach can improve the prediction accuracy.
基金supported by the National Natural Science Foundation of China(41601369)the Young Talents Program of Institute of Crop Sciences,Chinese Academy of Agricultural Sciences(S2019YC04)
文摘Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.
基金The National High Technology Research and Development Program of China (863 Program) (No.2008AA01Z227)the Cultivatable Fund of the Key Scientific and Technical Innovation Project of Ministry of Education of China (No.706028)
文摘In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. By the learning ability of the NN and the closely approximate unknown function to any degree of desired accuracy,the input-output mapping relationship between coordinates and the measurement data of time of arrival （TOA） and time difference of arrival （TDOA） is established. A real-time learning algorithm based on the extended Kalman filter （EKF） is used to train the multilayer perceptron （MLP） network by treating the linkweights of a network as the states of the nonlinear dynamic system. Since the EKF-based learning algorithm approximately gives the minimum variance estimate of the linkweights,the convergence is improved in comparison with the backwards error propagation （BP） algorithm. Numerical results illustrate thatthe proposedalgorithmcanachieve enhanced accuracy,and the performance ofthe algorithmis betterthanthat of the BP-based NN algorithm and the least squares （LS） algorithm in the NLOS environments. Moreover,this location method does not depend on a particular distribution of the NLOS error and does not need line-of-sight （ LOS ） or NLOS identification.
基金supported by Guangdong Provincial Technology Planning of China(Grant No.2007B010400052)State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body of China(Grant No.30715006)Guangdong Provincial Key Laboratory of Automotive Engineering,China(Grant No.2007A03012)
文摘For optimal design of mechanical clinching steel-aluminum joints,the back propagation(BP)neural network is used to research the mapping relationship between joining technique parameters including sheet thickness,sheet hardness,joint bottom diameter etc.,and mechanical properties of shearing and peeling in order to investigate joining technology between various material plates in the steel-aluminum hybrid structure car body.Genetic algorithm(GA)is adopted to optimize the back-propagation neural network connection weights.The training and validating samples are made by the BTM Tog-L-Loc system with different technologic parameters.The training samples'parameters and the corresponding joints'mechanical properties are supplied to the artificial neural network(ANN)for training.The validating samples'experimental data is used for checking up the prediction outputs.The calculation results show that GA can improve the model's prediction precision and generalization ability of BP neural network.The comparative analysis between the experimental data and the prediction outputs shows that ANN prediction models after training can effectively predict the mechanical properties of mechanical clinching joints and prove the feasibility and reliability of the intelligent neural networks system when used in the mechanical properties prediction of mechanical clinching joints.The prediction results can be used for a reference in the design of mechanical clinching steel-aluminum joints.
文摘While various kinds of fibers are used to improve the hot mix asphalt(HMA) performance, a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, the fatigue life of modified HMA samples using polypropylene and polyester fibers was evaluated and two models namely regression and artificial neural network(ANN) were used to predict the fatigue life based on the fibers parameters. As ANN contains many parameters such as the number of hidden layers which directly influence the prediction accuracy, genetic algorithm(GA) was used to solve optimization problem for ANN. Moreover, the trial and error method was used to optimize the GA parameters such as the population size. The comparison of the results obtained from regression and optimized ANN with GA shows that the two-hidden-layer ANN with two and five neurons in the first and second hidden layers, respectively, can predict the fatigue life of fiber-reinforced HMA with high accuracy(correlation coefficient of 0.96).
