Modern warfare demands weapons capable of penetrating substantial structures,which presents sig-nificant challenges to the reliability of the electronic devices that are crucial to the weapon's perfor-mance.Due to...Modern warfare demands weapons capable of penetrating substantial structures,which presents sig-nificant challenges to the reliability of the electronic devices that are crucial to the weapon's perfor-mance.Due to miniaturization of electronic components,it is challenging to directly measure or numerically predict the mechanical response of small-sized critical interconnections in board-level packaging structures to ensure the mechanical reliability of electronic devices in projectiles under harsh working conditions.To address this issue,an indirect measurement method using the Bayesian regularization-based load identification was proposed in this study based on finite element(FE)pre-dictions to estimate the load applied on critical interconnections of board-level packaging structures during the process of projectile penetration.For predicting the high-strain-rate penetration process,an FE model was established with elasto-plastic constitutive models of the representative packaging ma-terials(that is,solder material and epoxy molding compound)in which material constitutive parameters were calibrated against the experimental results by using the split-Hopkinson pressure bar.As the impact-induced dynamic bending of the printed circuit board resulted in an alternating tensile-compressive loading on the solder joints during penetration,the corner solder joints in the edge re-gions experience the highest S11 and strain,making them more prone to failure.Based on FE predictions at different structural scales,an improved Bayesian method based on augmented Tikhonov regulariza-tion was theoretically proposed to address the issues of ill-posed matrix inversion and noise sensitivity in the load identification at the critical solder joints.By incorporating a wavelet thresholding technique,the method resolves the problem of poor load identification accuracy at high noise levels.The proposed method achieves satisfactorily small relative errors and high correlation coefficients in identifying the mechanical response of local interconnections in board-level packaging structures,while significantly balancing the smoothness of response curves with the accuracy of peak identification.At medium and low noise levels,the relative error is less than 6%,while it is less than 10%at high noise levels.The proposed method provides an effective indirect approach for the boundary conditions of localized solder joints during the projectile penetration process,and its philosophy can be readily extended to other scenarios of multiscale analysis for highly nonlinear materials and structures under extreme loading conditions.展开更多
A neural network with a feed forward topology and Bayesian regularization training algorithm is used to predict the austenite formation temperatures （At1 and A13） by considering the percentage of alloying elements i...A neural network with a feed forward topology and Bayesian regularization training algorithm is used to predict the austenite formation temperatures （At1 and A13） by considering the percentage of alloying elements in chemical composition of steel. The data base used here involves a large variety of different steel types such as struc- tural steels, stainless steels, rail steels, spring steels, high temperature creep resisting steels and tool steels. Scatter diagrams and mean relative error （MRE） statistical criteria are used to compare the performance of developed neural network with the results of Andrew% empirical equations and a feed forward neural network with ＂gradient descent with momentum＂ training algorithm. The results showed that Bayesian regularization neural network has the best performance. Also, due to the satisfactory results of the developed neural network, it was used to investigate the effect of the chemical composition on Ac1 and At3 temperatures. Results are in accordance with materials science theories.展开更多
Soil compressibility parameters are important indicators in the geotechnical field and are affected by various factors such as natural conditions and human interference.When the sample size is too large,conventional m...Soil compressibility parameters are important indicators in the geotechnical field and are affected by various factors such as natural conditions and human interference.When the sample size is too large,conventional methods require massive human and financial resources.In order to reasonably simulate the compressibility parameters of the sample,this paper firstly adopts the correlation analysis to select seven influencing factors.Each of the factors has a high correlation with compressibility parameters.Meanwhile,the proportion of the weights of the seven factors in the Bayesian neural network is analyzed based on Garson theory.Secondly,an output model of the compressibility parameters of BR-BP silty clay is established based on Bayesian regularized BP neural network.Finally,the model is used to simulate the measured compressibility parameters.The output results are compared with the measured values and the output results of the traditional LM-BP neural network.The results show that the model is more stable and has stronger nonlinear fitting ability.The output of the model is basically consistent with the actual value.Compared with the traditional LMBP neural network model,its data sensitivity is enhanced,and the accuracy of the output result is significantly improved,the average value of the relative error of the compression coefficient is reduced from 15.54%to 6.15%,and the average value of the relative error of the compression modulus is reduced from 6.07%to 4.62%.The results provide a new technical method for obtaining the compressibility parameters of silty clay in this area,showing good theoretical significance and practical value.展开更多
Quantitative structure-activity relationship methods are used to study the quantitative structure tribo-ability relationship (QSTR), which refers to the tribology capability of a compound from the calculation of struc...Quantitative structure-activity relationship methods are used to study the quantitative structure tribo-ability relationship (QSTR), which refers to the tribology capability of a compound from the calculation of structure descriptors. Here, we used the Bayesian regularization neural network (BRNN) to establish a QSTR prediction model. Two-dimensional (2D) BRNN-QSTR models can flexibly and easily estimate lubricant-additive antiwear properties. Our results show that electron transfer and heteroatoms (such as S, P, O, and N) in a lubricant-additive molecule improve the antiwear ability. We also found that molecular connectivity indices are good descriptors of 2D BRNN-QSTR models.展开更多
Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the lim...Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs.Current energy efficiency strategies,such as clustering,multi-hop routing,and data aggregation,face challenges,including uneven energy depletion,high computational demands,and suboptimal cluster head(CH)selection.To address these limitations,this paper proposes a hybrid methodology that optimizes energy consumption(EC)while maintaining network performance.The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic(LEACH-D)protocol using an Artificial Neural Network(ANN)and Bayesian Regularization Algorithm(BRA).LEACH-D improves upon conventional LEACH by ensuring more uniform energy usage across SNs,mitigating inefficiencies from random CH selection.The ANN further enhances CH selection and routing processes,effectively reducing data transmission overhead and idle listening.Simulation results reveal that the LEACH-D-ANN model significantly reduces EC and extends the network’s lifespan compared to existing protocols.This framework offers a promising solution to the energy efficiency challenges in WSNs,paving the way for more sustainable and reliable network deployments.展开更多
Being a nonlinear operator,fractional derivatives can affect the enforcement of existence at any given time.As a result,the memory effect has an impact on all nonlinear processes modeled by fractional order differenti...Being a nonlinear operator,fractional derivatives can affect the enforcement of existence at any given time.As a result,the memory effect has an impact on all nonlinear processes modeled by fractional order differential equations(FODEs).The goal of this study is to increase the fractional model of the TB virus’s(FMTBV)accuracy.Stochastic solvers have never been used to solve FMTBV previously.The Bayesian regularized artificial(BRA)method and neural networks(NNs),often referred to as BRA-NNs,were used to solve the FMTBV model.Each scenario features five occurrences that each reflect a different order of derivatives,ranging from 0.8,0.85,0.9,0.95,and 1,as well as five potential rates for different parameters.Training data made up 90%of the data,testing data made up 5%,and validation data made up 5%of the data used to illustrate the FMTBV’s approximations.To verify that the BRA-NNs were correct,the generated simulations were described in the following solutions using the FOLotkaVolterra approach in MATLAB.Comprehensive Simulink results in terms of mean square error,error histogram,and regression analysis investigations further highlight the competence,dependability,and accuracy of the suggested BRA-NNs.展开更多
Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of t...Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS i1.0 software, the BRBPNN model was established between chlorophyll-α and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.000？8426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll- α declined in the order of alga amount 〉 secchi disc depth（SD） 〉 electrical conductivity （EC）. Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-α concentration, total phosphorus（TP） and total nitrogen（TN） were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data（chlorophyll-α prediction） and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake.展开更多
On the basis of artificial neural network (ANN) model, this paper presents an algorithm for inversing snow depth with use of AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System (EOS)) dataset, i.e., ...On the basis of artificial neural network (ANN) model, this paper presents an algorithm for inversing snow depth with use of AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System (EOS)) dataset, i.e., brightness temperature at 18.7 and 36.5GHz in Qinghai-Tibet Plateau during the snow season of 2002-2003. In order to overcome the overfitting problem in ANN modeling, this methodology adopts a Bayesian regularization approach. The experiments are performed to compare the results obtained from the ANN-based algorithm with those obtained from other existing algorithms, i.e., Chang algorithm, spectral polarization difference (SPD) algorithm, and temperature gradient (TG) algorithm. The experimental results show that the presented algorithm has the highest accuracy in estimating snow depth. In addition, the effects of the noises in datasets on model fitting can be decreased due to adopting the Bayesian regularization approach.展开更多
Introduction:Nowadays,the most significant challenges in the stock market is to predict the stock prices.The stock price data represents a financial time series data which becomes more difficult to predict due to its ...Introduction:Nowadays,the most significant challenges in the stock market is to predict the stock prices.The stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic nature.Case description:Support Vector Machines(SVM)and Artificial Neural Networks(ANN)are widely used for prediction of stock prices and its movements.Every algorithm has its way of learning patterns and then predicting.Artificial Neural Network(ANN)is a popular method which also incorporate technical analysis for making predictions in financial markets.Discussion and evaluation:Most common techniques used in the forecasting of financial time series are Support Vector Machine(SVM),Support Vector Regression(SVR)and Back Propagation Neural Network(BPNN).In this article,we use neural networks based on three different learning algorithms,i.e.,Levenberg-Marquardt,Scaled Conjugate Gradient and Bayesian Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results compared.Conclusion:All three algorithms provide an accuracy of 99.9%using tick data.The accuracy over 15-min dataset drops to 96.2%,97.0%and 98.9%for LM,SCG and Bayesian Regularization respectively which is significantly poor in comparison with that of results obtained using tick data.展开更多
Based on the idea of nonlinear prediction of phase space reconstruction, this paper presented a time delay BP neural network model, whose generalization capability was improved by Bayesian regularization. Furthermore,...Based on the idea of nonlinear prediction of phase space reconstruction, this paper presented a time delay BP neural network model, whose generalization capability was improved by Bayesian regularization. Furthermore, the model is applied to forecast the import and export trades in one industry. The results showed that the improved model has excellent generalization capabilities, which not only learned the historical curve, but efficiently predicted the trend of business. Comparing with common evaluation of forecasts, we put on a conclusion that nonlinear forecast can not only focus on data combination and precision improvement, it also can vividly reflect the nonlinear characteristic of the forecas ting system. While analyzing the forecasting precision of the model, we give a model judgment by calculating the nonlinear characteristic value of the combined serial and original serial, proved that the forecasting model can reasonably catch＇ the dynamic characteristic of the nonlinear system which produced the origin serial.展开更多
As the conventional prediction methods for production of waterflooding reservoirs have some drawbacks, a production forecasting model based on artificial neural network was proposed, the simulation process by this met...As the conventional prediction methods for production of waterflooding reservoirs have some drawbacks, a production forecasting model based on artificial neural network was proposed, the simulation process by this method was presented, and some examples were illustrated. A workflow that involves a physics-based extraction of features was proposed for fluid production forecasting to improve the prediction effect. The Bayesian regularization algorithm was selected as the training algorithm of the model. This algorithm, although taking longer time, can better generalize oil, gas and water production data sets. The model was evaluated by calculating mean square error and determination coefficient, drawing error distribution histogram and the cross-plot between simulation data and verification data etc. The model structure was trained, validated and tested with 90% of the historical data, and blindly evaluated using the remaining. The predictive model consumes minimal information and computational cost and is capable of predicting fluid production rate with a coefficient of determination of more than 0.9, which has the simulation results consistent with the practical data.展开更多
Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of ground...Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of groundwater resources. In this study, a new nonlinear autoregressive with exogenous inputs(NARX) network has been applied to simulate monthly groundwater levels in a well of Sylhet Sadar at a local scale. The Levenberg-Marquardt(LM) and Bayesian Regularization(BR) algorithms were used to train the NARX network, and the results were compared to determine the best architecture for predicting monthly groundwater levels over time. The comparison between LM and BR showed that NARX-BR has advantages over predicting monthly levels based on the Mean Squared Error(MSE), coefficient of determination(R^2), and Nash-Sutcliffe coefficient of efficiency(NSE). The results show that BR is the most accurate method for predicting groundwater levels with an error of ± 0.35 m. This method is applied to the management of irrigation water source, which provides important information for the prediction of local groundwater fluctuation at local level during a short period.展开更多
Natural stones used as floor and wall coverings are exposed to many different abrasive forces,so it is essential to choose suitable materials for wear resistance in terms of the life of the structure.The abrasion resi...Natural stones used as floor and wall coverings are exposed to many different abrasive forces,so it is essential to choose suitable materials for wear resistance in terms of the life of the structure.The abrasion resistance of natural stones can be determined in the laboratory by applying the B?hme abrasion resistance(BAR)test.However,the direct analysis of BAR in the laboratory has disadvantages such as wasting time and energy,experimental errors,and health impacts.To eliminate these disadvantages,the estimation of BAR using artificial neural networks(ANN)was proposed.Different natural stone samples were collected from Türkiye,and uniaxial compressive strength(UCS),flexural strength(FS),water absorption rate(WA),unit volume weight(UW),effective porosity(n),and BAR tests were carried out.The outputs of these tests were gathered and a data set,consisting of a total of 105 data,was randomly divided into two groups:testing and training.In the current study,the success of three different training algorithms of Levenberg-Marquardt(LM),Bayesian regularization(BR),and scaled conjugate gradient(SCG)were compared for BAR prediction of natural stones.Statistical criteria such as coefficient of determination(R~2),mean square error(MSE),mean square error(RMSE),and mean absolute percentage error(MAPE),which are widely used and adopted in the literature,were used to determine predictive validity.The findings of the study indicated that ANN is a valid method for estimating the BAR value.Also,the LM algorithm(R~2=0.9999,MSE=0.0001,RMSE=0.0110,and MAPE=0.0487)in training and the BR algorithm(R~2=0.9896,MSE=0.0589,RMSE=0.2427,and MAPE=1.2327)in testing showed the best prediction performance.It has been observed that the proposed method is quite practical to implement.Using the artificial neural networks method will provide an advantage in similar laborintensive experimental studies.展开更多
A novel smoothness term of Bayesian regularization framework based on M-estimation of robust statistics is proposed, and from this term a class of fourth-order nonlinear diffusion methods is proposed. These methods at...A novel smoothness term of Bayesian regularization framework based on M-estimation of robust statistics is proposed, and from this term a class of fourth-order nonlinear diffusion methods is proposed. These methods attempt to approximate an observed image with a piecewise linear image, which looks more natural than piecewise constant image used to approximate an observed image by P-M model. It is known that M-estimators and W-estimators are essentially equivalent and solve the same minimization problem. Then, we propose PL bilateral filter from equivalent W-estimator. This new model is designed for piecewise linear image filtering, which is more effective than normal bilateral filter.展开更多
This article is intended as a proposal for a numerical model for the prediction of the ultimate moment of a reinforced concrete beam reinforced with composite materials based on neural networks, which are classified i...This article is intended as a proposal for a numerical model for the prediction of the ultimate moment of a reinforced concrete beam reinforced with composite materials based on neural networks, which are classified in the artificial intelligence method. In this work, a RBF network or radial basis function type model was created and tested. The validation of the RBF architecture consists in judging its predictive capacity by using the weights and biases computed during the training, to apply them to another database which did not participate to the training and testing of the model. So, with Bayesian regularization, a maximum error of 0.0813 Tm in absolute value was found between the targets and predicted outputs. The value of the mean square error MSE = 1.1106 * 10<sup>-4</sup> allowed us to quantify and justify the prediction performance of this network. Through this article, RBF network model was justified perform and can be used and exploited by our engineers with a high reliability rate.展开更多
Oncolytic viral immunotherapy is gaining considerable prominence in the realm of chronic diseases treatment and rehabilitation.Oncolytic viral therapy is an intriguing therapeutic approach due to its low toxicity and ...Oncolytic viral immunotherapy is gaining considerable prominence in the realm of chronic diseases treatment and rehabilitation.Oncolytic viral therapy is an intriguing therapeutic approach due to its low toxicity and dual function of immune stimulations.This work aims to design a soft computing approach using stupendous knacks of neural networks(NNs)optimized with Bayesian regularization(BR),i.e.NNs-BR,procedure.The constructed NNs-BR technique is exploited in order to determine the approximate numerical treatment of the nonlinear multi-delayed tumor virotherapy(TVT)models in terms of the dynamic interactions between the tumor cells free of viruses,tumor cells infected by viruses,viruses,and cytotoxic T-lymphocytes(CTLs).The strength of state-of-the-art numerical approach is incorporated to develop the reference dataset for the variation in the infection rate for tumor cells,virus-free tumor cell clearance rate by CTLs,CTLs clearance rate for infectious tumor cells,the natural lifecycle of infectious tumor cells,the natural lifecycle of viral cell,the natural lifecycle of CTLs cells,tumor cells free of viruses'maximum proliferation rate,production of tumor cells with an infection,CTLs simulated ratio for infectious tumor cells,CTLs simulated ratios for virus-free cells and delay in time.The dataset is randomly chosen/segmented for training-testing-validation samples to construct the NNs models optimized with backpropagated BR representing the approximate numerical solutions of the dynamic interactions in the TVT model.The performance of the designed NNs-BR technique is accessed/evaluated and outcomes are found in good agreement with the reference solutions having the range of accuracy from 10^(-9) to 10^(-16).The eficacy of NNs-BR paradigm is further substantiated after rigorous analysis on regression metrics,learning curves on MSE,and error histograms for the dynamics of TVT model.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52475166,52175148)the Regional Collaboration Project of Shanxi Province(Grant No.202204041101044).
文摘Modern warfare demands weapons capable of penetrating substantial structures,which presents sig-nificant challenges to the reliability of the electronic devices that are crucial to the weapon's perfor-mance.Due to miniaturization of electronic components,it is challenging to directly measure or numerically predict the mechanical response of small-sized critical interconnections in board-level packaging structures to ensure the mechanical reliability of electronic devices in projectiles under harsh working conditions.To address this issue,an indirect measurement method using the Bayesian regularization-based load identification was proposed in this study based on finite element(FE)pre-dictions to estimate the load applied on critical interconnections of board-level packaging structures during the process of projectile penetration.For predicting the high-strain-rate penetration process,an FE model was established with elasto-plastic constitutive models of the representative packaging ma-terials(that is,solder material and epoxy molding compound)in which material constitutive parameters were calibrated against the experimental results by using the split-Hopkinson pressure bar.As the impact-induced dynamic bending of the printed circuit board resulted in an alternating tensile-compressive loading on the solder joints during penetration,the corner solder joints in the edge re-gions experience the highest S11 and strain,making them more prone to failure.Based on FE predictions at different structural scales,an improved Bayesian method based on augmented Tikhonov regulariza-tion was theoretically proposed to address the issues of ill-posed matrix inversion and noise sensitivity in the load identification at the critical solder joints.By incorporating a wavelet thresholding technique,the method resolves the problem of poor load identification accuracy at high noise levels.The proposed method achieves satisfactorily small relative errors and high correlation coefficients in identifying the mechanical response of local interconnections in board-level packaging structures,while significantly balancing the smoothness of response curves with the accuracy of peak identification.At medium and low noise levels,the relative error is less than 6%,while it is less than 10%at high noise levels.The proposed method provides an effective indirect approach for the boundary conditions of localized solder joints during the projectile penetration process,and its philosophy can be readily extended to other scenarios of multiscale analysis for highly nonlinear materials and structures under extreme loading conditions.
基金Financial support of mechanical engineering center of excellence at Roudbar Azad University
文摘A neural network with a feed forward topology and Bayesian regularization training algorithm is used to predict the austenite formation temperatures （At1 and A13） by considering the percentage of alloying elements in chemical composition of steel. The data base used here involves a large variety of different steel types such as struc- tural steels, stainless steels, rail steels, spring steels, high temperature creep resisting steels and tool steels. Scatter diagrams and mean relative error （MRE） statistical criteria are used to compare the performance of developed neural network with the results of Andrew% empirical equations and a feed forward neural network with ＂gradient descent with momentum＂ training algorithm. The results showed that Bayesian regularization neural network has the best performance. Also, due to the satisfactory results of the developed neural network, it was used to investigate the effect of the chemical composition on Ac1 and At3 temperatures. Results are in accordance with materials science theories.
基金This project is sponsored by the Basic scientific research business funding project of Institute of Seismic Prediction,CEA(2018 IESLZ06)the Natural Science Foundation of China(51778590)Earthquake Science and Technology Spark Project(XH20057)。
文摘Soil compressibility parameters are important indicators in the geotechnical field and are affected by various factors such as natural conditions and human interference.When the sample size is too large,conventional methods require massive human and financial resources.In order to reasonably simulate the compressibility parameters of the sample,this paper firstly adopts the correlation analysis to select seven influencing factors.Each of the factors has a high correlation with compressibility parameters.Meanwhile,the proportion of the weights of the seven factors in the Bayesian neural network is analyzed based on Garson theory.Secondly,an output model of the compressibility parameters of BR-BP silty clay is established based on Bayesian regularized BP neural network.Finally,the model is used to simulate the measured compressibility parameters.The output results are compared with the measured values and the output results of the traditional LM-BP neural network.The results show that the model is more stable and has stronger nonlinear fitting ability.The output of the model is basically consistent with the actual value.Compared with the traditional LMBP neural network model,its data sensitivity is enhanced,and the accuracy of the output result is significantly improved,the average value of the relative error of the compression coefficient is reduced from 15.54%to 6.15%,and the average value of the relative error of the compression modulus is reduced from 6.07%to 4.62%.The results provide a new technical method for obtaining the compressibility parameters of silty clay in this area,showing good theoretical significance and practical value.
基金the National Basic Research (973) Program of China，the National Natural Science Foundation of China
文摘Quantitative structure-activity relationship methods are used to study the quantitative structure tribo-ability relationship (QSTR), which refers to the tribology capability of a compound from the calculation of structure descriptors. Here, we used the Bayesian regularization neural network (BRNN) to establish a QSTR prediction model. Two-dimensional (2D) BRNN-QSTR models can flexibly and easily estimate lubricant-additive antiwear properties. Our results show that electron transfer and heteroatoms (such as S, P, O, and N) in a lubricant-additive molecule improve the antiwear ability. We also found that molecular connectivity indices are good descriptors of 2D BRNN-QSTR models.
文摘Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs.Current energy efficiency strategies,such as clustering,multi-hop routing,and data aggregation,face challenges,including uneven energy depletion,high computational demands,and suboptimal cluster head(CH)selection.To address these limitations,this paper proposes a hybrid methodology that optimizes energy consumption(EC)while maintaining network performance.The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic(LEACH-D)protocol using an Artificial Neural Network(ANN)and Bayesian Regularization Algorithm(BRA).LEACH-D improves upon conventional LEACH by ensuring more uniform energy usage across SNs,mitigating inefficiencies from random CH selection.The ANN further enhances CH selection and routing processes,effectively reducing data transmission overhead and idle listening.Simulation results reveal that the LEACH-D-ANN model significantly reduces EC and extends the network’s lifespan compared to existing protocols.This framework offers a promising solution to the energy efficiency challenges in WSNs,paving the way for more sustainable and reliable network deployments.
基金supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2024/R/1445).
文摘Being a nonlinear operator,fractional derivatives can affect the enforcement of existence at any given time.As a result,the memory effect has an impact on all nonlinear processes modeled by fractional order differential equations(FODEs).The goal of this study is to increase the fractional model of the TB virus’s(FMTBV)accuracy.Stochastic solvers have never been used to solve FMTBV previously.The Bayesian regularized artificial(BRA)method and neural networks(NNs),often referred to as BRA-NNs,were used to solve the FMTBV model.Each scenario features five occurrences that each reflect a different order of derivatives,ranging from 0.8,0.85,0.9,0.95,and 1,as well as five potential rates for different parameters.Training data made up 90%of the data,testing data made up 5%,and validation data made up 5%of the data used to illustrate the FMTBV’s approximations.To verify that the BRA-NNs were correct,the generated simulations were described in the following solutions using the FOLotkaVolterra approach in MATLAB.Comprehensive Simulink results in terms of mean square error,error histogram,and regression analysis investigations further highlight the competence,dependability,and accuracy of the suggested BRA-NNs.
文摘Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS i1.0 software, the BRBPNN model was established between chlorophyll-α and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.000？8426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll- α declined in the order of alga amount 〉 secchi disc depth（SD） 〉 electrical conductivity （EC）. Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-α concentration, total phosphorus（TP） and total nitrogen（TN） were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data（chlorophyll-α prediction） and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake.
基金Under the auspices of Special Basic Research Fund for Central Public Scientific Research Institutes (No. 2007-03)
文摘On the basis of artificial neural network (ANN) model, this paper presents an algorithm for inversing snow depth with use of AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System (EOS)) dataset, i.e., brightness temperature at 18.7 and 36.5GHz in Qinghai-Tibet Plateau during the snow season of 2002-2003. In order to overcome the overfitting problem in ANN modeling, this methodology adopts a Bayesian regularization approach. The experiments are performed to compare the results obtained from the ANN-based algorithm with those obtained from other existing algorithms, i.e., Chang algorithm, spectral polarization difference (SPD) algorithm, and temperature gradient (TG) algorithm. The experimental results show that the presented algorithm has the highest accuracy in estimating snow depth. In addition, the effects of the noises in datasets on model fitting can be decreased due to adopting the Bayesian regularization approach.
文摘Introduction:Nowadays,the most significant challenges in the stock market is to predict the stock prices.The stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic nature.Case description:Support Vector Machines(SVM)and Artificial Neural Networks(ANN)are widely used for prediction of stock prices and its movements.Every algorithm has its way of learning patterns and then predicting.Artificial Neural Network(ANN)is a popular method which also incorporate technical analysis for making predictions in financial markets.Discussion and evaluation:Most common techniques used in the forecasting of financial time series are Support Vector Machine(SVM),Support Vector Regression(SVR)and Back Propagation Neural Network(BPNN).In this article,we use neural networks based on three different learning algorithms,i.e.,Levenberg-Marquardt,Scaled Conjugate Gradient and Bayesian Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results compared.Conclusion:All three algorithms provide an accuracy of 99.9%using tick data.The accuracy over 15-min dataset drops to 96.2%,97.0%and 98.9%for LM,SCG and Bayesian Regularization respectively which is significantly poor in comparison with that of results obtained using tick data.
基金Supported bythe Natural Science Foundation of Hunan Province(2001ABB006 ,2003ABA043)
文摘Based on the idea of nonlinear prediction of phase space reconstruction, this paper presented a time delay BP neural network model, whose generalization capability was improved by Bayesian regularization. Furthermore, the model is applied to forecast the import and export trades in one industry. The results showed that the improved model has excellent generalization capabilities, which not only learned the historical curve, but efficiently predicted the trend of business. Comparing with common evaluation of forecasts, we put on a conclusion that nonlinear forecast can not only focus on data combination and precision improvement, it also can vividly reflect the nonlinear characteristic of the forecas ting system. While analyzing the forecasting precision of the model, we give a model judgment by calculating the nonlinear characteristic value of the combined serial and original serial, proved that the forecasting model can reasonably catch＇ the dynamic characteristic of the nonlinear system which produced the origin serial.
文摘As the conventional prediction methods for production of waterflooding reservoirs have some drawbacks, a production forecasting model based on artificial neural network was proposed, the simulation process by this method was presented, and some examples were illustrated. A workflow that involves a physics-based extraction of features was proposed for fluid production forecasting to improve the prediction effect. The Bayesian regularization algorithm was selected as the training algorithm of the model. This algorithm, although taking longer time, can better generalize oil, gas and water production data sets. The model was evaluated by calculating mean square error and determination coefficient, drawing error distribution histogram and the cross-plot between simulation data and verification data etc. The model structure was trained, validated and tested with 90% of the historical data, and blindly evaluated using the remaining. The predictive model consumes minimal information and computational cost and is capable of predicting fluid production rate with a coefficient of determination of more than 0.9, which has the simulation results consistent with the practical data.
文摘Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of groundwater resources. In this study, a new nonlinear autoregressive with exogenous inputs(NARX) network has been applied to simulate monthly groundwater levels in a well of Sylhet Sadar at a local scale. The Levenberg-Marquardt(LM) and Bayesian Regularization(BR) algorithms were used to train the NARX network, and the results were compared to determine the best architecture for predicting monthly groundwater levels over time. The comparison between LM and BR showed that NARX-BR has advantages over predicting monthly levels based on the Mean Squared Error(MSE), coefficient of determination(R^2), and Nash-Sutcliffe coefficient of efficiency(NSE). The results show that BR is the most accurate method for predicting groundwater levels with an error of ± 0.35 m. This method is applied to the management of irrigation water source, which provides important information for the prediction of local groundwater fluctuation at local level during a short period.
文摘Natural stones used as floor and wall coverings are exposed to many different abrasive forces,so it is essential to choose suitable materials for wear resistance in terms of the life of the structure.The abrasion resistance of natural stones can be determined in the laboratory by applying the B?hme abrasion resistance(BAR)test.However,the direct analysis of BAR in the laboratory has disadvantages such as wasting time and energy,experimental errors,and health impacts.To eliminate these disadvantages,the estimation of BAR using artificial neural networks(ANN)was proposed.Different natural stone samples were collected from Türkiye,and uniaxial compressive strength(UCS),flexural strength(FS),water absorption rate(WA),unit volume weight(UW),effective porosity(n),and BAR tests were carried out.The outputs of these tests were gathered and a data set,consisting of a total of 105 data,was randomly divided into two groups:testing and training.In the current study,the success of three different training algorithms of Levenberg-Marquardt(LM),Bayesian regularization(BR),and scaled conjugate gradient(SCG)were compared for BAR prediction of natural stones.Statistical criteria such as coefficient of determination(R~2),mean square error(MSE),mean square error(RMSE),and mean absolute percentage error(MAPE),which are widely used and adopted in the literature,were used to determine predictive validity.The findings of the study indicated that ANN is a valid method for estimating the BAR value.Also,the LM algorithm(R~2=0.9999,MSE=0.0001,RMSE=0.0110,and MAPE=0.0487)in training and the BR algorithm(R~2=0.9896,MSE=0.0589,RMSE=0.2427,and MAPE=1.2327)in testing showed the best prediction performance.It has been observed that the proposed method is quite practical to implement.Using the artificial neural networks method will provide an advantage in similar laborintensive experimental studies.
文摘A novel smoothness term of Bayesian regularization framework based on M-estimation of robust statistics is proposed, and from this term a class of fourth-order nonlinear diffusion methods is proposed. These methods attempt to approximate an observed image with a piecewise linear image, which looks more natural than piecewise constant image used to approximate an observed image by P-M model. It is known that M-estimators and W-estimators are essentially equivalent and solve the same minimization problem. Then, we propose PL bilateral filter from equivalent W-estimator. This new model is designed for piecewise linear image filtering, which is more effective than normal bilateral filter.
文摘This article is intended as a proposal for a numerical model for the prediction of the ultimate moment of a reinforced concrete beam reinforced with composite materials based on neural networks, which are classified in the artificial intelligence method. In this work, a RBF network or radial basis function type model was created and tested. The validation of the RBF architecture consists in judging its predictive capacity by using the weights and biases computed during the training, to apply them to another database which did not participate to the training and testing of the model. So, with Bayesian regularization, a maximum error of 0.0813 Tm in absolute value was found between the targets and predicted outputs. The value of the mean square error MSE = 1.1106 * 10<sup>-4</sup> allowed us to quantify and justify the prediction performance of this network. Through this article, RBF network model was justified perform and can be used and exploited by our engineers with a high reliability rate.
文摘Oncolytic viral immunotherapy is gaining considerable prominence in the realm of chronic diseases treatment and rehabilitation.Oncolytic viral therapy is an intriguing therapeutic approach due to its low toxicity and dual function of immune stimulations.This work aims to design a soft computing approach using stupendous knacks of neural networks(NNs)optimized with Bayesian regularization(BR),i.e.NNs-BR,procedure.The constructed NNs-BR technique is exploited in order to determine the approximate numerical treatment of the nonlinear multi-delayed tumor virotherapy(TVT)models in terms of the dynamic interactions between the tumor cells free of viruses,tumor cells infected by viruses,viruses,and cytotoxic T-lymphocytes(CTLs).The strength of state-of-the-art numerical approach is incorporated to develop the reference dataset for the variation in the infection rate for tumor cells,virus-free tumor cell clearance rate by CTLs,CTLs clearance rate for infectious tumor cells,the natural lifecycle of infectious tumor cells,the natural lifecycle of viral cell,the natural lifecycle of CTLs cells,tumor cells free of viruses'maximum proliferation rate,production of tumor cells with an infection,CTLs simulated ratio for infectious tumor cells,CTLs simulated ratios for virus-free cells and delay in time.The dataset is randomly chosen/segmented for training-testing-validation samples to construct the NNs models optimized with backpropagated BR representing the approximate numerical solutions of the dynamic interactions in the TVT model.The performance of the designed NNs-BR technique is accessed/evaluated and outcomes are found in good agreement with the reference solutions having the range of accuracy from 10^(-9) to 10^(-16).The eficacy of NNs-BR paradigm is further substantiated after rigorous analysis on regression metrics,learning curves on MSE,and error histograms for the dynamics of TVT model.
