A multilayer recurrent fuzzy neural network(MRFNN)is proposed for accurate dynamic system modeling.The proposed MRFNN has six layers combined with T-S fuzzy model.The recurrent structures are formed by local feedback ...A multilayer recurrent fuzzy neural network(MRFNN)is proposed for accurate dynamic system modeling.The proposed MRFNN has six layers combined with T-S fuzzy model.The recurrent structures are formed by local feedback connections in the membership layer and the rule layer.With these feedbacks,the fuzzy sets are time-varying and the temporal problem of dynamic system can be solved well.The parameters of MRFNN are learned by chaotic search(CS)and least square estimation(LSE)simultaneously,where CS is for tuning the premise parameters and LSE is for updating the consequent coefficients accordingly.Results of simulations show the proposed approach is effective for dynamic system modeling with high accuracy.展开更多
To implement a quantificational evaluation for mechanical kinematic scheme more effectively,a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory. Firstly,the structure ...To implement a quantificational evaluation for mechanical kinematic scheme more effectively,a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory. Firstly,the structure of evaluation model is constructed according to evaluation indicator system. Then evaluation samples are generated and provided to train this model. Thus it can reflect the relation between attributive value and evaluation result,as well as the weight of evaluation indicator. Once evaluation indicators of each candidate are fuzzily quantified and fed into the trained network model,the corresponding evaluation result is outputted and the best alternative can be selected. Under this model,expert knowledge can be effectively acquired and expressed,and the quantificational evaluation can be implemented for kinematic scheme with multi-level evaluation indicator system. Several key problems on this model are discussed and an illustration has demonstrated that this model is feasible and can be regarded as a new idea for solving kinematic scheme evaluation.展开更多
Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper descri...Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper describes an improved SPSA algorithm, which entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability. The proposed fuzzy adaptive simultaneous perturbation approximation （FASPA） algorithm is particularly well suited to problems involving a large number of parameters such as those encountered in nonlinear system identification using neural networks （NNs）. Accordingly, a multilayer perceptron （MLP） network with popular training algorithms was used to predicate the system response. We found that an MLP trained by FASPSA had the desired accuracy that was comparable to results obtained by traditional system identification algorithms. Simulation results for typical nonlinear systems demonstrate that the proposed NN architecture trained with FASPSA yields improved system identification as measured by reduced time of convergence and a smaller identification error.展开更多
The problem of global robust asymptotical stability for a class of Takagi-Sugeno fuzzy neural networks（TSFNN） with discontinuous activation functions and time delays is investigated by using Lyapunov stability theor...The problem of global robust asymptotical stability for a class of Takagi-Sugeno fuzzy neural networks（TSFNN） with discontinuous activation functions and time delays is investigated by using Lyapunov stability theory.Based on linear matrix inequalities（LMIs）,we originally propose robust fuzzy control to guarantee the global robust asymptotical stability of TSFNNs.Compared with the existing literature,this paper removes the assumptions on the neuron activations such as Lipschitz conditions,bounded,monotonic increasing property or the right-limit value is bigger than the left one at the discontinuous point.Thus,the results are more general and wider.Finally,two numerical examples are given to show the effectiveness of the proposed stability results.展开更多
Multiple sensor registration is an important link in multi-sensors data fusion. The existed algorithm is all based on the assumption that system errors come from a fixed deviation set. But there are many other factors...Multiple sensor registration is an important link in multi-sensors data fusion. The existed algorithm is all based on the assumption that system errors come from a fixed deviation set. But there are many other factors, which can result system errors. So traditional registration algorithms have limitation. This paper presents a registration algorithm for sensor alignment based on stochastic fuzzy neural network (SNFF), and utilized fuzzy clustering algorithm obtaining the number of fuzzy rules. Finally, the simulative result illuminate that this way could gain a satisfing result.展开更多
To design a multi-population adaptive genetic BP algorithm, crossover probability and mutation probability are self-adjusted according to the standard deviation of population fitness in this paper. Then a hybrid model...To design a multi-population adaptive genetic BP algorithm, crossover probability and mutation probability are self-adjusted according to the standard deviation of population fitness in this paper. Then a hybrid model combining Fuzzy Neural Network and multi-population adaptive genetic BP algorithm—Adaptive Genetic Fuzzy Neural Network (AGFNN) is proposed to overcome Neural Network’s drawbacks. Furthermore, the new model has been applied to financial distress prediction and the effectiveness of the proposed model is performed on the data collected from a set of Chinese listed corporations using cross validation approach. A comparative result indicates that the performance of AGFNN model is much better than the ones of other neural network models.展开更多
Aiming at the problems that fuzzy neural network controller has heavy computation and lag,a T-S norm Fuzzy Neural Network Control based on hybrid learning algorithm was proposed.Immune genetic algorithm (IGA) was used...Aiming at the problems that fuzzy neural network controller has heavy computation and lag,a T-S norm Fuzzy Neural Network Control based on hybrid learning algorithm was proposed.Immune genetic algorithm (IGA) was used to optimize the parameters of membership functions (MFs) off line,and the neural network was used to adjust the parameters of MFs on line to enhance the response of the controller.Moreover,the latter network was used to adjust the fuzzy rules automatically to reduce the computation of the neural network and improve the robustness and adaptability of the controller,so that the controller can work well ever when the underwater vehicle works in hostile ocean environment.Finally,experiments were carried on " XX" mini autonomous underwater vehicle (min-AUV) in tank.The results showed that this controller has great improvement in response and overshoot,compared with the traditional controllers.展开更多
A new neural network model termed ‘standard neural network model’ (SNNM) is presented, and a state-feedback control law is then designed for the SNNM to stabilize the closed-loop system. The control design constrain...A new neural network model termed ‘standard neural network model’ (SNNM) is presented, and a state-feedback control law is then designed for the SNNM to stabilize the closed-loop system. The control design constraints are shown to be a set of linear matrix inequalities (LMIs), which can be easily solved by the MATLAB LMI Control Toolbox to determine the control law. Most recurrent neural networks (including the chaotic neural network) and nonlinear systems modeled by neural networks or Takagi and Sugeno (T-S) fuzzy models can be transformed into the SNNMs to be stabilization controllers synthesized in the framework of a unified SNNM. Finally, three numerical examples are provided to illustrate the design developed in this paper.展开更多
Due to continuing aging and heavy utilization of many bridges and the limited available funds, the importance of proper bridge condition state assessment has risen recently, which is the crucial point for rational dec...Due to continuing aging and heavy utilization of many bridges and the limited available funds, the importance of proper bridge condition state assessment has risen recently, which is the crucial point for rational decision-making on MR&R activities. This paper presents a prototype of the concrete bridge condition state assessment system (CBCSAS) with the following sub-modules: inspection, parameter recognition, structural assessment, main cause identification and priority-to-action. And multi-layer neural networks, which may combine with fuzzy set theory or not, are performed to realize the structural assessment with embedding expert knowledge into the assessment system.展开更多
文摘A multilayer recurrent fuzzy neural network(MRFNN)is proposed for accurate dynamic system modeling.The proposed MRFNN has six layers combined with T-S fuzzy model.The recurrent structures are formed by local feedback connections in the membership layer and the rule layer.With these feedbacks,the fuzzy sets are time-varying and the temporal problem of dynamic system can be solved well.The parameters of MRFNN are learned by chaotic search(CS)and least square estimation(LSE)simultaneously,where CS is for tuning the premise parameters and LSE is for updating the consequent coefficients accordingly.Results of simulations show the proposed approach is effective for dynamic system modeling with high accuracy.
基金Supported by the Shanxi Natural Science Foundation under contract number 20041070 and Natural Science Foundation of north u-niversity of China .
文摘To implement a quantificational evaluation for mechanical kinematic scheme more effectively,a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory. Firstly,the structure of evaluation model is constructed according to evaluation indicator system. Then evaluation samples are generated and provided to train this model. Thus it can reflect the relation between attributive value and evaluation result,as well as the weight of evaluation indicator. Once evaluation indicators of each candidate are fuzzily quantified and fed into the trained network model,the corresponding evaluation result is outputted and the best alternative can be selected. Under this model,expert knowledge can be effectively acquired and expressed,and the quantificational evaluation can be implemented for kinematic scheme with multi-level evaluation indicator system. Several key problems on this model are discussed and an illustration has demonstrated that this model is feasible and can be regarded as a new idea for solving kinematic scheme evaluation.
文摘Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper describes an improved SPSA algorithm, which entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability. The proposed fuzzy adaptive simultaneous perturbation approximation （FASPA） algorithm is particularly well suited to problems involving a large number of parameters such as those encountered in nonlinear system identification using neural networks （NNs）. Accordingly, a multilayer perceptron （MLP） network with popular training algorithms was used to predicate the system response. We found that an MLP trained by FASPSA had the desired accuracy that was comparable to results obtained by traditional system identification algorithms. Simulation results for typical nonlinear systems demonstrate that the proposed NN architecture trained with FASPSA yields improved system identification as measured by reduced time of convergence and a smaller identification error.
基金supported by the National Natural Science Foundation of China(6077504760835004)+2 种基金the National High Technology Research and Development Program of China(863 Program)(2007AA04Z244 2008AA04Z214)the Graduate Innovation Fundation of Hunan Province(CX2010B132)
文摘The problem of global robust asymptotical stability for a class of Takagi-Sugeno fuzzy neural networks（TSFNN） with discontinuous activation functions and time delays is investigated by using Lyapunov stability theory.Based on linear matrix inequalities（LMIs）,we originally propose robust fuzzy control to guarantee the global robust asymptotical stability of TSFNNs.Compared with the existing literature,this paper removes the assumptions on the neuron activations such as Lipschitz conditions,bounded,monotonic increasing property or the right-limit value is bigger than the left one at the discontinuous point.Thus,the results are more general and wider.Finally,two numerical examples are given to show the effectiveness of the proposed stability results.
文摘Multiple sensor registration is an important link in multi-sensors data fusion. The existed algorithm is all based on the assumption that system errors come from a fixed deviation set. But there are many other factors, which can result system errors. So traditional registration algorithms have limitation. This paper presents a registration algorithm for sensor alignment based on stochastic fuzzy neural network (SNFF), and utilized fuzzy clustering algorithm obtaining the number of fuzzy rules. Finally, the simulative result illuminate that this way could gain a satisfing result.
文摘To design a multi-population adaptive genetic BP algorithm, crossover probability and mutation probability are self-adjusted according to the standard deviation of population fitness in this paper. Then a hybrid model combining Fuzzy Neural Network and multi-population adaptive genetic BP algorithm—Adaptive Genetic Fuzzy Neural Network (AGFNN) is proposed to overcome Neural Network’s drawbacks. Furthermore, the new model has been applied to financial distress prediction and the effectiveness of the proposed model is performed on the data collected from a set of Chinese listed corporations using cross validation approach. A comparative result indicates that the performance of AGFNN model is much better than the ones of other neural network models.
文摘Aiming at the problems that fuzzy neural network controller has heavy computation and lag,a T-S norm Fuzzy Neural Network Control based on hybrid learning algorithm was proposed.Immune genetic algorithm (IGA) was used to optimize the parameters of membership functions (MFs) off line,and the neural network was used to adjust the parameters of MFs on line to enhance the response of the controller.Moreover,the latter network was used to adjust the fuzzy rules automatically to reduce the computation of the neural network and improve the robustness and adaptability of the controller,so that the controller can work well ever when the underwater vehicle works in hostile ocean environment.Finally,experiments were carried on " XX" mini autonomous underwater vehicle (min-AUV) in tank.The results showed that this controller has great improvement in response and overshoot,compared with the traditional controllers.
基金the National Natural Science Foundation of China (No. 60504024)the Specialized Research Fund for the Doc-toral Program of Higher Education, China (No. 20060335022)+1 种基金the Natural Science Foundation of Zhejiang Province, China (No. Y106010)the "151 Talent Project" of Zhejiang Province (Nos. 05-3-1013 and 06-2-034), China
文摘A new neural network model termed ‘standard neural network model’ (SNNM) is presented, and a state-feedback control law is then designed for the SNNM to stabilize the closed-loop system. The control design constraints are shown to be a set of linear matrix inequalities (LMIs), which can be easily solved by the MATLAB LMI Control Toolbox to determine the control law. Most recurrent neural networks (including the chaotic neural network) and nonlinear systems modeled by neural networks or Takagi and Sugeno (T-S) fuzzy models can be transformed into the SNNMs to be stabilization controllers synthesized in the framework of a unified SNNM. Finally, three numerical examples are provided to illustrate the design developed in this paper.
文摘Due to continuing aging and heavy utilization of many bridges and the limited available funds, the importance of proper bridge condition state assessment has risen recently, which is the crucial point for rational decision-making on MR&R activities. This paper presents a prototype of the concrete bridge condition state assessment system (CBCSAS) with the following sub-modules: inspection, parameter recognition, structural assessment, main cause identification and priority-to-action. And multi-layer neural networks, which may combine with fuzzy set theory or not, are performed to realize the structural assessment with embedding expert knowledge into the assessment system.
