Artificial neural network is a new approach to pattern recognition and classification. The model of multilayer perceptron （MLP） and back-propagation （BP） is used to train the algorithm in the artificial neural net...Artificial neural network is a new approach to pattern recognition and classification. The model of multilayer perceptron （MLP） and back-propagation （BP） is used to train the algorithm in the artificial neural network. An improved fast algorithm of the BP network was presented, which adopts a singular value decomposition （SVD） and a generalized inverse matrix. It not only increases the speed of network learning but also achieves a satisfying precision. The simulation and experiment results show the effect of improvement of BP algorithm on the classification of the surface defects of steel plate.展开更多
Up to now, some technology of neural networks are developed to solve the non-linearity of researched objects and to implement the adaptive control in many engineering fields, and some good results were achieved. Thoug...Up to now, some technology of neural networks are developed to solve the non-linearity of researched objects and to implement the adaptive control in many engineering fields, and some good results were achieved. Though it puts some questions over to design application structure with neural networks, it is really unknowable about the study mechanism of those. But, the importance of study ratio is widely realized by many scientists now, and some methods on the modification of that are provided. The main subject is how to improve the stability and how to increase the convergent rate of networks by defining a good form of the study ratio. Here a new algorithm named LDBP (least disturbance BP algorithm) is proposed to calculate the ratio online according to the output errors, the weights of network and the input values. The algorithm is applied to the control of an autonomous underwater vehicle designed by HEU. The experimental results show that the algorithm has good performance and the controller designed based on it is fine.展开更多
Back propagation (BP) algorithm is a very useful algorithm in many areas, but its leaning process is a very complicated non linear convergence process, in which, chaos often happens, and slow convergence speed and loc...Back propagation (BP) algorithm is a very useful algorithm in many areas, but its leaning process is a very complicated non linear convergence process, in which, chaos often happens, and slow convergence speed and local least often make it difficult for the non experts to use it widely, and an improved BP (IBP) algorithm is therefore suggested to expedite the convergence speed. The algorithm can judge local least and take some steps automatically to jump out from the local least. Furthermore, this algorithm introduces the expert knowledge base. An IBP based agile and current neural network (NN) constructed tool is designed. An initial NN can be constructed automatically using an expert knowledge base. And an Aitken’s Δ 2 process method is used to expedite the convergent speed for NN. Besides, the method of changing the parameter of Sigmoid function and increasing the hidden node is used to bring surge for NN to jump out from the local展开更多
Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University,the analysis model scheme of energy consumption of individual buildings in the university is studied by us...Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University,the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation(BP)neural network to solve nonlinear problems and have the ability of global approximation and generalization.By analyzing the influence of different uses,different building surfaces and different energysaving schemes on the change of building energy consumption,the grey correlation method is used to determine the main influencing factors affecting each building energy consumption,including uses,building surfaces and energy-saving schemes,which are used as the input of the model and the building energy consumption as the output of the model,so as to establish the building energy consumption analysis model based on BP neural network.However,in practical application,BP neural network has the defects of slow convergence and easy to fall into local minima.In view of this,this paper uses genetic algorithm to optimize the weight and threshold of BP neural network,completes the improvement of various building energy consumption analysis models,and realizes the qualitative analysis of building energy consumption.The model verification results show that the viscosity of the building energy consumption analysis model based on genetic algorithm improved BP neural network algorithm(GABP)in this paper is relatively high,which is more accurate than the results of the traditional BP neural network model,and the relative error of the analysis model is reduced from 11.56%to 8.13%,which proves that the GABP can be better suitable for the study of school building energy consumption analysis model,It is applied to the prediction of building energy consumption,which lays a foundation for the realization of carbon neutralization in the South expansion plan of Yangtze University.展开更多
A simple new BP algorithm named circle BP algorithm is introduced.With this algorithm,local minimums can be completely got rid of and learning speed can improve dramatically.It can be easily designed into the circuitr...A simple new BP algorithm named circle BP algorithm is introduced.With this algorithm,local minimums can be completely got rid of and learning speed can improve dramatically.It can be easily designed into the circuitry and advance further the application of MLP neural network .展开更多
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
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
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
In the process of Wavelet Analysis,only the low-frequency signals are re-decomposed,and the high-frequency signals are no longer decomposed,resulting in a decrease in frequency resolution with increasing frequency.The...In the process of Wavelet Analysis,only the low-frequency signals are re-decomposed,and the high-frequency signals are no longer decomposed,resulting in a decrease in frequency resolution with increasing frequency.Therefore,in this paper,firstly,Wavelet Packet Decomposition is used for feature extraction of vibration signals,which makes up for the shortcomings of Wavelet Analysis in extracting fault features of nonlinear vibration signals,and different energy values in different frequency bands are obtained by Wavelet Packet Decomposition.The features are visualized by the K-Means clustering method,and the results show that the extracted energy features can accurately distinguish the different states of the bearing.Then a fault diagnosis model based on BP Neural Network optimized by Beetle Algo-rithm is proposed to identify the bearing faults.Compared with the Particle Swarm Algorithm,Beetle Algorithm can quickly find the error extreme value,which greatly reduces the training time of the model.At last,two experiments are conducted,which show that the accuracy of the model can reach more than 95%,and the model has a certain anti-interference ability.展开更多
To reduce the bandwidth and storage resources of image information in communication transmission, and improve the secure communication of information. In this paper, an image compression and encryption algorithm based...To reduce the bandwidth and storage resources of image information in communication transmission, and improve the secure communication of information. In this paper, an image compression and encryption algorithm based on fractional-order memristive hyperchaotic system and BP neural network is proposed. In this algorithm, the image pixel values are compressed by BP neural network, the chaotic sequences of the fractional-order memristive hyperchaotic system are used to diffuse the pixel values. The experimental simulation results indicate that the proposed algorithm not only can effectively compress and encrypt image, but also have better security features. Therefore, this work provides theoretical guidance and experimental basis for the safe transmission and storage of image information in practical communication.展开更多
Because of complexity and non-predictability of the tunnel surrounding rock,the problem with the determination of the physical and mechanical parameters of the surrounding rock has become a main obstacle to theoretica...Because of complexity and non-predictability of the tunnel surrounding rock,the problem with the determination of the physical and mechanical parameters of the surrounding rock has become a main obstacle to theoretical research and numerical analysis in tunnel engineering.During design,it is a frequent practice,therefore,to give recommended values by analog based on experience.It is a key point in current research to make use of the displacement back analytic method to comparatively accurately determine the parameters of the surrounding rock whereas artificial intelligence possesses an exceptionally strong capability of identifying,expressing and coping with such complex non-linear relationships.The parameters can be verified by searching the optimal network structure,using back analysis on measured data to search optimal parameters and performing direct computation of the obtained results.In the current paper,the direct analysis is performed with the biological emulation system and the software of Fast Lagrangian Analysis of Continua(FLAC3D.The high non-linearity,network reasoning and coupling ability of the neural network are employed.The output vector required of the training of the neural network is obtained with the numerical analysis software.And the overall space search is conducted by employing the Adaptive Immunity Algorithm.As a result,we are able to avoid the shortcoming that multiple parameters and optimized parameters are easy to fall into a local extremum.At the same time,the computing speed and efficiency are increased as well.Further,in the paper satisfactory conclusions are arrived at through the intelligent direct-back analysis on the monitored and measured data at the Erdaoya tunneling project.The results show that the physical and mechanical parameters obtained by the intelligent direct-back analysis proposed in the current paper have effectively improved the recommended values in the original prospecting data.This is of practical significance to the appraisal of stability and informationization design of the surrounding rock.展开更多
Topography can strongly affect ground motion,and studies of the quantification of hill surfaces’topographic effect are relatively rare.In this paper,a new quantitative seismic topographic effect prediction method bas...Topography can strongly affect ground motion,and studies of the quantification of hill surfaces’topographic effect are relatively rare.In this paper,a new quantitative seismic topographic effect prediction method based upon the BP neural network algorithm and three-dimensional finite element method(FEM)was developed.The FEM simulation results were compared with seismic records and the results show that the PGA and response spectra have a tendency to increase with increasing elevation,but the correlation between PGA amplification factors and slope is not obvious for low hills.New BP neural network models were established for the prediction of amplification factors of PGA and response spectra.Two kinds of input variables’combinations which are convenient to achieve are proposed in this paper for the prediction of amplification factors of PGA and response spectra,respectively.The absolute values of prediction errors can be mostly within 0.1 for PGA amplification factors,and they can be mostly within 0.2 for response spectra’s amplification factors.One input variables’combination can achieve better prediction performance while the other one has better expandability of the predictive region.Particularly,the BP models only employ one hidden layer with about a hundred nodes,which makes it efficient for training.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 heating technological requirement of the conventional PID control is difficult to guarantee which based on the precise mathematical model,because the heating furnace for heating treatment with the big inertia,the ...The heating technological requirement of the conventional PID control is difficult to guarantee which based on the precise mathematical model,because the heating furnace for heating treatment with the big inertia,the pure time delay and nonlinear time-varying.Proposed one kind optimized variable method of PID controller based on the genetic algorithm with improved BP network that better realized the completely automatic intelligent control of the entire thermal process than the classics critical purporting(Z-N)method.A heating furnace for the object was simulated with MATLAB,simulation results show that the control system has the quicker response characteristic,the better dynamic characteristic and the quite stronger robustness,which has some promotional value for the control of industrial furnace.展开更多
The advantages and disadvantages of genetic algorithm and BP algorithm are introduced. A neural network based on GA-BP algorithm is proposed and applied in the prediction of protein secondary structure, which combines...The advantages and disadvantages of genetic algorithm and BP algorithm are introduced. A neural network based on GA-BP algorithm is proposed and applied in the prediction of protein secondary structure, which combines the advantages of BP and GA. The prediction and training on the neural network are made respectively based on 4 structure classifications of protein so as to get higher rate of predication---the highest prediction rate 75.65%,the average prediction rate 65.04%.展开更多
With more and more researches about improving BP algorithm, there are more improvement methods. The paper researches two improvement algorithms based on quasi-Newton method, DFP algorithm and L-BFGS algorithm. After f...With more and more researches about improving BP algorithm, there are more improvement methods. The paper researches two improvement algorithms based on quasi-Newton method, DFP algorithm and L-BFGS algorithm. After fully analyzing the features of quasi- Newton methods, the paper improves BP neural network algorithm. And the adjustment is made for the problems in the improvement process. The paper makes empirical analysis and proves the effectiveness of BP neural network algorithm based on quasi-Newton method. The improved algorithms are compared with the traditional BP algorithm, which indicates that the imoroved BP algorithm is better.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
The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A a...The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A and signal constant n in traditional signal propagation path loss models.This algorithm utilizes the adaptive whale optimization algorithm to iteratively optimize the parameters of the backpropagation(BP)neural network,thereby enhancing its prediction performance.To address the issue of low accuracy and large errors in traditional received signal strength indication(RSSI),the algorithm first uses the extended Kalman filtering model to smooth the RSSI signal values to suppress the influence of noise and outliers on the estimation results.The processed RSSI values are used as inputs to the neural network,with distance values as outputs,resulting in more accurate ranging results.Finally,the position of the node to be measured is determined by combining the weighted centroid algorithm.Experimental simulation results show that compared to the standard centroid algorithm,weighted centroid algorithm,BP weighted centroid algorithm,and whale optimization algorithm(WOA)-BP weighted centroid algorithm,the proposed algorithm reduces the average localization error by 58.23%,42.71%,31.89%,and 17.57%,respectively,validating the effectiveness and superiority of the algorithm.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
基金Item Sponsored by National Natural Science Foundation of China (60277029)
文摘Artificial neural network is a new approach to pattern recognition and classification. The model of multilayer perceptron （MLP） and back-propagation （BP） is used to train the algorithm in the artificial neural network. An improved fast algorithm of the BP network was presented, which adopts a singular value decomposition （SVD） and a generalized inverse matrix. It not only increases the speed of network learning but also achieves a satisfying precision. The simulation and experiment results show the effect of improvement of BP algorithm on the classification of the surface defects of steel plate.
文摘Up to now, some technology of neural networks are developed to solve the non-linearity of researched objects and to implement the adaptive control in many engineering fields, and some good results were achieved. Though it puts some questions over to design application structure with neural networks, it is really unknowable about the study mechanism of those. But, the importance of study ratio is widely realized by many scientists now, and some methods on the modification of that are provided. The main subject is how to improve the stability and how to increase the convergent rate of networks by defining a good form of the study ratio. Here a new algorithm named LDBP (least disturbance BP algorithm) is proposed to calculate the ratio online according to the output errors, the weights of network and the input values. The algorithm is applied to the control of an autonomous underwater vehicle designed by HEU. The experimental results show that the algorithm has good performance and the controller designed based on it is fine.
文摘Back propagation (BP) algorithm is a very useful algorithm in many areas, but its leaning process is a very complicated non linear convergence process, in which, chaos often happens, and slow convergence speed and local least often make it difficult for the non experts to use it widely, and an improved BP (IBP) algorithm is therefore suggested to expedite the convergence speed. The algorithm can judge local least and take some steps automatically to jump out from the local least. Furthermore, this algorithm introduces the expert knowledge base. An IBP based agile and current neural network (NN) constructed tool is designed. An initial NN can be constructed automatically using an expert knowledge base. And an Aitken’s Δ 2 process method is used to expedite the convergent speed for NN. Besides, the method of changing the parameter of Sigmoid function and increasing the hidden node is used to bring surge for NN to jump out from the local
基金The authors received the sources of funding of a project,The Name:Special Project for Innovation and Entrepreneurship Education Reform in Hubei Province Colleges and Universities(2020),Item Number:136/5013602701.
文摘Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University,the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation(BP)neural network to solve nonlinear problems and have the ability of global approximation and generalization.By analyzing the influence of different uses,different building surfaces and different energysaving schemes on the change of building energy consumption,the grey correlation method is used to determine the main influencing factors affecting each building energy consumption,including uses,building surfaces and energy-saving schemes,which are used as the input of the model and the building energy consumption as the output of the model,so as to establish the building energy consumption analysis model based on BP neural network.However,in practical application,BP neural network has the defects of slow convergence and easy to fall into local minima.In view of this,this paper uses genetic algorithm to optimize the weight and threshold of BP neural network,completes the improvement of various building energy consumption analysis models,and realizes the qualitative analysis of building energy consumption.The model verification results show that the viscosity of the building energy consumption analysis model based on genetic algorithm improved BP neural network algorithm(GABP)in this paper is relatively high,which is more accurate than the results of the traditional BP neural network model,and the relative error of the analysis model is reduced from 11.56%to 8.13%,which proves that the GABP can be better suitable for the study of school building energy consumption analysis model,It is applied to the prediction of building energy consumption,which lays a foundation for the realization of carbon neutralization in the South expansion plan of Yangtze University.
文摘A simple new BP algorithm named circle BP algorithm is introduced.With this algorithm,local minimums can be completely got rid of and learning speed can improve dramatically.It can be easily designed into the circuitry and advance further the application of MLP neural network .
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.
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.
基金Supported by Agricultural Science and Technology Independent Innovation Fund of Jiangsu Province of China(Grant No.CX(19)3081)Key Research and Development Program of Jiangsu Province of China(Grant No.BE2018127).
文摘In the process of Wavelet Analysis,only the low-frequency signals are re-decomposed,and the high-frequency signals are no longer decomposed,resulting in a decrease in frequency resolution with increasing frequency.Therefore,in this paper,firstly,Wavelet Packet Decomposition is used for feature extraction of vibration signals,which makes up for the shortcomings of Wavelet Analysis in extracting fault features of nonlinear vibration signals,and different energy values in different frequency bands are obtained by Wavelet Packet Decomposition.The features are visualized by the K-Means clustering method,and the results show that the extracted energy features can accurately distinguish the different states of the bearing.Then a fault diagnosis model based on BP Neural Network optimized by Beetle Algo-rithm is proposed to identify the bearing faults.Compared with the Particle Swarm Algorithm,Beetle Algorithm can quickly find the error extreme value,which greatly reduces the training time of the model.At last,two experiments are conducted,which show that the accuracy of the model can reach more than 95%,and the model has a certain anti-interference ability.
基金the Basic Scientific Research Projects of Colleges and Universities of Liaoning Province (Grant Nos. 2017J045)Provincial Natural Science Foundation of Liaoning (Grant Nos. 20170540060)
文摘To reduce the bandwidth and storage resources of image information in communication transmission, and improve the secure communication of information. In this paper, an image compression and encryption algorithm based on fractional-order memristive hyperchaotic system and BP neural network is proposed. In this algorithm, the image pixel values are compressed by BP neural network, the chaotic sequences of the fractional-order memristive hyperchaotic system are used to diffuse the pixel values. The experimental simulation results indicate that the proposed algorithm not only can effectively compress and encrypt image, but also have better security features. Therefore, this work provides theoretical guidance and experimental basis for the safe transmission and storage of image information in practical communication.
基金supported by the National Natural Science Foundation of China(No.50609028)
文摘Because of complexity and non-predictability of the tunnel surrounding rock,the problem with the determination of the physical and mechanical parameters of the surrounding rock has become a main obstacle to theoretical research and numerical analysis in tunnel engineering.During design,it is a frequent practice,therefore,to give recommended values by analog based on experience.It is a key point in current research to make use of the displacement back analytic method to comparatively accurately determine the parameters of the surrounding rock whereas artificial intelligence possesses an exceptionally strong capability of identifying,expressing and coping with such complex non-linear relationships.The parameters can be verified by searching the optimal network structure,using back analysis on measured data to search optimal parameters and performing direct computation of the obtained results.In the current paper,the direct analysis is performed with the biological emulation system and the software of Fast Lagrangian Analysis of Continua(FLAC3D.The high non-linearity,network reasoning and coupling ability of the neural network are employed.The output vector required of the training of the neural network is obtained with the numerical analysis software.And the overall space search is conducted by employing the Adaptive Immunity Algorithm.As a result,we are able to avoid the shortcoming that multiple parameters and optimized parameters are easy to fall into a local extremum.At the same time,the computing speed and efficiency are increased as well.Further,in the paper satisfactory conclusions are arrived at through the intelligent direct-back analysis on the monitored and measured data at the Erdaoya tunneling project.The results show that the physical and mechanical parameters obtained by the intelligent direct-back analysis proposed in the current paper have effectively improved the recommended values in the original prospecting data.This is of practical significance to the appraisal of stability and informationization design of the surrounding rock.
基金supported by the National Natural Science Foundation of China(No.51878625)the Collaboratory for the Study of Earthquake Predictability in China Seismic Experimental Site(No.2018YFE0109700)the General Scientific Research Foundation of Shandong Earthquake Agency(No.YB2208).
文摘Topography can strongly affect ground motion,and studies of the quantification of hill surfaces’topographic effect are relatively rare.In this paper,a new quantitative seismic topographic effect prediction method based upon the BP neural network algorithm and three-dimensional finite element method(FEM)was developed.The FEM simulation results were compared with seismic records and the results show that the PGA and response spectra have a tendency to increase with increasing elevation,but the correlation between PGA amplification factors and slope is not obvious for low hills.New BP neural network models were established for the prediction of amplification factors of PGA and response spectra.Two kinds of input variables’combinations which are convenient to achieve are proposed in this paper for the prediction of amplification factors of PGA and response spectra,respectively.The absolute values of prediction errors can be mostly within 0.1 for PGA amplification factors,and they can be mostly within 0.2 for response spectra’s amplification factors.One input variables’combination can achieve better prediction performance while the other one has better expandability of the predictive region.Particularly,the BP models only employ one hidden layer with about a hundred nodes,which makes it efficient for training.
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 work was supported by the youth backbone teachers training program of Henan colleges and universities under Grant No.2016ggjs-287the project of science and technology of Henan province under Grant No.172102210124the Key Scientific Research projects in Colleges and Universities in Henan(Grant No.18B460003).
文摘The heating technological requirement of the conventional PID control is difficult to guarantee which based on the precise mathematical model,because the heating furnace for heating treatment with the big inertia,the pure time delay and nonlinear time-varying.Proposed one kind optimized variable method of PID controller based on the genetic algorithm with improved BP network that better realized the completely automatic intelligent control of the entire thermal process than the classics critical purporting(Z-N)method.A heating furnace for the object was simulated with MATLAB,simulation results show that the control system has the quicker response characteristic,the better dynamic characteristic and the quite stronger robustness,which has some promotional value for the control of industrial furnace.
文摘The advantages and disadvantages of genetic algorithm and BP algorithm are introduced. A neural network based on GA-BP algorithm is proposed and applied in the prediction of protein secondary structure, which combines the advantages of BP and GA. The prediction and training on the neural network are made respectively based on 4 structure classifications of protein so as to get higher rate of predication---the highest prediction rate 75.65%,the average prediction rate 65.04%.
文摘With more and more researches about improving BP algorithm, there are more improvement methods. The paper researches two improvement algorithms based on quasi-Newton method, DFP algorithm and L-BFGS algorithm. After fully analyzing the features of quasi- Newton methods, the paper improves BP neural network algorithm. And the adjustment is made for the problems in the improvement process. The paper makes empirical analysis and proves the effectiveness of BP neural network algorithm based on quasi-Newton method. The improved algorithms are compared with the traditional BP algorithm, which indicates that the imoroved BP algorithm is better.
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.
基金supported by the National Natural Science Foundation of China(Nos.62265010,62061024)Gansu Province Science and Technology Plan(No.23YFGA0062)Gansu Province Innovation Fund(No.2022A-215)。
文摘The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A and signal constant n in traditional signal propagation path loss models.This algorithm utilizes the adaptive whale optimization algorithm to iteratively optimize the parameters of the backpropagation(BP)neural network,thereby enhancing its prediction performance.To address the issue of low accuracy and large errors in traditional received signal strength indication(RSSI),the algorithm first uses the extended Kalman filtering model to smooth the RSSI signal values to suppress the influence of noise and outliers on the estimation results.The processed RSSI values are used as inputs to the neural network,with distance values as outputs,resulting in more accurate ranging results.Finally,the position of the node to be measured is determined by combining the weighted centroid algorithm.Experimental simulation results show that compared to the standard centroid algorithm,weighted centroid algorithm,BP weighted centroid algorithm,and whale optimization algorithm(WOA)-BP weighted centroid algorithm,the proposed algorithm reduces the average localization error by 58.23%,42.71%,31.89%,and 17.57%,respectively,validating the effectiveness and superiority of the algorithm.
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...
