One kind of steepest descent incremental projection learning algorithm for improving the training of radial basis function(RBF)neural network is proposed,which is applied to analog circuit fault isolation.This algorit...One kind of steepest descent incremental projection learning algorithm for improving the training of radial basis function(RBF)neural network is proposed,which is applied to analog circuit fault isolation.This algorithm simplified the structure of network through optimum output layer coefficient with incremental projection learning(IPL)algorithm,and adjusted the parameters of the neural activation function to control the network scale and improve the network approximation ability.Compared to the traditional algorithm,the improved algorithm has quicker convergence rate and higher isolation precision.Simulation results show that this improved RBF network has much better performance,which can be used in analog circuit fault isolation field.展开更多
A constructive-pruning hybrid method （CPHM） for radial basis function （RBF） networks is proposed to improve the prediction accuracy of ash fusion temperatures （AFT）. The CPHM incorporates the advantages of the c...A constructive-pruning hybrid method （CPHM） for radial basis function （RBF） networks is proposed to improve the prediction accuracy of ash fusion temperatures （AFT）. The CPHM incorporates the advantages of the construction algorithm and the pruning algorithm of neural networks, and the training process of the CPHM is divided into two stages： rough tuning and fine tuning. In rough tuning, new hidden units are added to the current network until some performance index is satisfied. In fine tuning, the network structure and the model parameters are further adjusted. And, based on components of coal ash, a model using the CPHM is established to predict the AFT. The results show that the CPHM prediction model is characterized by its high precision, compact network structure, as well as strong generalization ability and robustness.展开更多
Objective This paper presents classifications of m ental tasks based on EEG signals using an adaptive Radial Basis Function (RBF) n etwork with optimal centers and widths for the Brain-Computer Interface (BCI) s che...Objective This paper presents classifications of m ental tasks based on EEG signals using an adaptive Radial Basis Function (RBF) n etwork with optimal centers and widths for the Brain-Computer Interface (BCI) s chemes. Methods Initial centers and widths of the network are s elected by a cluster estimation method based on the distribution of the training set. Using a conjugate gradient descent method, they are optimized during train ing phase according to a regularized error function considering the influence of their changes to output values. Results The optimizing process improves the performance of RBF network, and its best cognition rate of three t ask pairs over four subjects achieves 87.0%. Moreover, this network runs fast du e to the fewer hidden layer neurons. Conclusion The adaptive RB F network with optimal centers and widths has high recognition rate and runs fas t. It may be a promising classifier for on-line BCI scheme.展开更多
Based on immune clustering and evolutionary programming(EP), a hybrid algorithm to train the RBF network is proposed. An immune fuzzy C-means clustering algorithm (IFCM) is used to adaptively specify the amount and in...Based on immune clustering and evolutionary programming(EP), a hybrid algorithm to train the RBF network is proposed. An immune fuzzy C-means clustering algorithm (IFCM) is used to adaptively specify the amount and initial positions of the RBF centers according to input data set; then the RBF network is trained with EP that tends to global optima. The application of the hybrid algorithm in multiuser detection problem demonstrates that the RBF network trained with the algorithm has simple network structure with good generalization ability.展开更多
We extract some physical and chemical features re-lated to the occurrence of single nucleotide polymorphism （SNP） from three groups of sliding windows around SNP site,and then make the predictions about accuracy by ...We extract some physical and chemical features re-lated to the occurrence of single nucleotide polymorphism （SNP） from three groups of sliding windows around SNP site,and then make the predictions about accuracy by using radial basis function （RBF） networks. The result of the forward sliding windows sug-gests that the accuracies and Matthews correlation coefficient （MCC values） ascend with the increasing of length of sliding windows. The accuracies range from 73.27 % to 80.69 %,and MCC values range from 0.465 to 0.614. The backward sliding windows and the sliding windows with fixed length three are de-signed to find the crucial sites related to SNP. The results imply that the occurrence possibility of SNP relies heavily on the above physical and chemical features of sites which are at a distance around 20 bases from the SNP site. Compared with the support vector machine （SVM）,our RBF network approach has achieved more satisfactory results.展开更多
Accurate performance prediction of Grid workflow activities can help Grid schedulers map activitiesto appropriate Grid sites.This paper describes an approach based on features-ranked RBF neural networkto predict the p...Accurate performance prediction of Grid workflow activities can help Grid schedulers map activitiesto appropriate Grid sites.This paper describes an approach based on features-ranked RBF neural networkto predict the performance of Grid workflow activities.Experimental results for two kinds of real worldGrid workflow activities are presented to show effectiveness of our approach.展开更多
This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was t...This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely.展开更多
In space operation,flexible manipulators and gripper mechanisms have been widely used because of light weight and flexibility.However,the vibration caused by slender structures in manipulators and the parameter pertur...In space operation,flexible manipulators and gripper mechanisms have been widely used because of light weight and flexibility.However,the vibration caused by slender structures in manipulators and the parameter perturbation caused by the uncertainty derived from grasping mass variation cannot be ignored.The existence of vibration and parameter perturbation makes the rotation control of flexible manipulators difficult,which seriously affects the operation accuracy of manipulators.What’s more,the complex dynamic coupling brings great challenges to the dynamics modeling and vibration analysis.To solve this problem,this paper takes the space flexible manipulator with an underactuated hand(SFMUH)as the research object.The dynamics model considering flexibility,multiple nonlinear elements and disturbance torque is established by the assumed modal method(AMM)and Hamilton’s principle.A dynamic modeling simplification method is proposed by analyzing the nonlinear terms.What’s more,a sliding mode control(SMC)method combined with the radial basis function(RBF)neural network compensation is proposed.Besides,the control law is designed using a saturation function in the control method to weaken the chatter phenomenon.With the help of neural networks to identify the uncertainty composition in the SFMUH,the tracking accuracy is improved.The results of ground control experiments verify the advantages of the control method for vibration suppression of the SFMUH.展开更多
基金Pre-research Projects Fund of the National Ar ming Department,the 11th Five-year Projects
文摘One kind of steepest descent incremental projection learning algorithm for improving the training of radial basis function(RBF)neural network is proposed,which is applied to analog circuit fault isolation.This algorithm simplified the structure of network through optimum output layer coefficient with incremental projection learning(IPL)algorithm,and adjusted the parameters of the neural activation function to control the network scale and improve the network approximation ability.Compared to the traditional algorithm,the improved algorithm has quicker convergence rate and higher isolation precision.Simulation results show that this improved RBF network has much better performance,which can be used in analog circuit fault isolation field.
基金The National Natural Science Foundation of China(No.60875035)the Natural Science Foundation of Jiangsu Province(No.BK2008294)the National High Technology Research and Development Program of China(863 Program)(No.2006AA05A107)
文摘A constructive-pruning hybrid method （CPHM） for radial basis function （RBF） networks is proposed to improve the prediction accuracy of ash fusion temperatures （AFT）. The CPHM incorporates the advantages of the construction algorithm and the pruning algorithm of neural networks, and the training process of the CPHM is divided into two stages： rough tuning and fine tuning. In rough tuning, new hidden units are added to the current network until some performance index is satisfied. In fine tuning, the network structure and the model parameters are further adjusted. And, based on components of coal ash, a model using the CPHM is established to predict the AFT. The results show that the CPHM prediction model is characterized by its high precision, compact network structure, as well as strong generalization ability and robustness.
基金ThisworkwassupportedbytheNationalNaturalScienceFoundationofChina (No .3 0 3 70 3 95 )
文摘Objective This paper presents classifications of m ental tasks based on EEG signals using an adaptive Radial Basis Function (RBF) n etwork with optimal centers and widths for the Brain-Computer Interface (BCI) s chemes. Methods Initial centers and widths of the network are s elected by a cluster estimation method based on the distribution of the training set. Using a conjugate gradient descent method, they are optimized during train ing phase according to a regularized error function considering the influence of their changes to output values. Results The optimizing process improves the performance of RBF network, and its best cognition rate of three t ask pairs over four subjects achieves 87.0%. Moreover, this network runs fast du e to the fewer hidden layer neurons. Conclusion The adaptive RB F network with optimal centers and widths has high recognition rate and runs fas t. It may be a promising classifier for on-line BCI scheme.
文摘Based on immune clustering and evolutionary programming(EP), a hybrid algorithm to train the RBF network is proposed. An immune fuzzy C-means clustering algorithm (IFCM) is used to adaptively specify the amount and initial positions of the RBF centers according to input data set; then the RBF network is trained with EP that tends to global optima. The application of the hybrid algorithm in multiuser detection problem demonstrates that the RBF network trained with the algorithm has simple network structure with good generalization ability.
基金Supported by Discipline-Crossing Research Foundation of Huazhong Agricultural University(2008XKJC006)the Fundamental Research Funds for the Central Universities of China
文摘We extract some physical and chemical features re-lated to the occurrence of single nucleotide polymorphism （SNP） from three groups of sliding windows around SNP site,and then make the predictions about accuracy by using radial basis function （RBF） networks. The result of the forward sliding windows sug-gests that the accuracies and Matthews correlation coefficient （MCC values） ascend with the increasing of length of sliding windows. The accuracies range from 73.27 % to 80.69 %,and MCC values range from 0.465 to 0.614. The backward sliding windows and the sliding windows with fixed length three are de-signed to find the crucial sites related to SNP. The results imply that the occurrence possibility of SNP relies heavily on the above physical and chemical features of sites which are at a distance around 20 bases from the SNP site. Compared with the support vector machine （SVM）,our RBF network approach has achieved more satisfactory results.
基金Supported by the European Union through the IST-034601 edutain@grid project
文摘Accurate performance prediction of Grid workflow activities can help Grid schedulers map activitiesto appropriate Grid sites.This paper describes an approach based on features-ranked RBF neural networkto predict the performance of Grid workflow activities.Experimental results for two kinds of real worldGrid workflow activities are presented to show effectiveness of our approach.
基金The National High Technology Research and Development Program of China (863 Program) (No.2003AA517020)
文摘This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely.
基金supported by the National Natural Science Foundation of China(No.52275090)the Fundamental Research Funds for the Central Universities(No.N2103025)+1 种基金the National Key Research and Development Program of China(No.2020YFB2007802)the Applied Basic Research Program of Liaoning Province(No.2023JH2/101300159)。
文摘In space operation,flexible manipulators and gripper mechanisms have been widely used because of light weight and flexibility.However,the vibration caused by slender structures in manipulators and the parameter perturbation caused by the uncertainty derived from grasping mass variation cannot be ignored.The existence of vibration and parameter perturbation makes the rotation control of flexible manipulators difficult,which seriously affects the operation accuracy of manipulators.What’s more,the complex dynamic coupling brings great challenges to the dynamics modeling and vibration analysis.To solve this problem,this paper takes the space flexible manipulator with an underactuated hand(SFMUH)as the research object.The dynamics model considering flexibility,multiple nonlinear elements and disturbance torque is established by the assumed modal method(AMM)and Hamilton’s principle.A dynamic modeling simplification method is proposed by analyzing the nonlinear terms.What’s more,a sliding mode control(SMC)method combined with the radial basis function(RBF)neural network compensation is proposed.Besides,the control law is designed using a saturation function in the control method to weaken the chatter phenomenon.With the help of neural networks to identify the uncertainty composition in the SFMUH,the tracking accuracy is improved.The results of ground control experiments verify the advantages of the control method for vibration suppression of the SFMUH.
