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.
Supported by a grant from the research fund of State Key Laboratory of Automobile Safety & Energy Conservatio (No. KF2005-006) and the China Postdoctoral Science Foundation (No.20030304145).
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Wang, J., Chen, Q. (2005). Stochastic Fuzzy Neural Network and Its Robust Parameter Learning Algorithm. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_98
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DOI: https://doi.org/10.1007/11427391_98
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
Online ISBN: 978-3-540-32065-4
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