Stochastic fuzzy neural network and its robust parameter learning algorithm

Authors:
Junping Wang;Quanshi Chen
Affiliations:
State Key Laboratory of Automobile Safety & Energy Conservation, Tsinghua University, Beijing, China;State Key Laboratory of Automobile Safety & Energy Conservation, Tsinghua University, Beijing, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

Citing 2
Cited 0

Adaptive fuzzy systems and control: design and stability analysis

Adaptive fuzzy systems and control: design and stability analysis
Learning neural networks with noisy inputs using the errors-in-variables approach

IEEE Transactions on Neural Networks

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Abstract

A Stochastic Fuzzy Neural Network (SFNN) which has filtering effect on noisy input is studied. the structure of the SFNN is mended and the nodes in each layer of the SFNN are discussed. Each layer in the new structure has exact physical meaning. The number of the nodes is decreased, so is the computation amount. In the parameter learning algorithm, if noisy input data is used the LS cost function based method can cause severe biasing effects. This problem can be solved by a novel EIV cost function which contains the error variables. In this paper, the cost function is extended to multi-input single output system, and the error variables are obtained through learning algorithm to avoid repeated measurement. This method was used to train the parameters of the SFNN. The simulation results show the efficiency of this algorithm.