Efficient learning algorithms for three-layer regular feedforward fuzzy neural networks

IEEE Transactions on Neural Networks
Puyin Liu, Hongxing Li

Abstract

A key step of using gradient descend methods to develop learning algorithms of a regular feedforward fuzzy neural network (FNN) is to differentiate max--min functions, which contain max and min operations. The paper aims at several objectives. First, investigate further the differentiation of max--min functions. Second, employ general fuzzy numbers, which include triangular and trapezoidal fuzzy numbers as special cases to define a three-layer regular FNN. The general fuzzy numbers related can be approximately determined by their corresponding finite level sets. So, we can approximately represent the input-output (I/O) relationship of the regular FNN as functions of the endpoints of all finite level sets. Third, a fuzzy back-propagation algorithm is presented. And to speed up the convergence of the learning algorithm, a fuzzy conjugate gradient algorithm for fuzzy weights and biases is developed, furthermore, the convergence of the algorithm is analyzed, systematically. Finally, some real simulations demonstrate the efficiency of our learning algorithms. The regular FNN is applied to the approximate realization of fuzzy inference rules and fuzzy functions defined on given compact sets.

References

May 23, 2002·Neural Networks : the Official Journal of the International Neural Network Society·Sukumar ChakrabortyNikhil R Pal
Mar 29, 2003·Neural Networks : the Official Journal of the International Neural Network Society·Ah Chung Tsoi, Franco Scarselli
Jan 1, 1996·IEEE Transactions on Neural Networks·X ZhangP Z Wang
Jan 1, 1994·IEEE Transactions on Neural Networks·D S Chen, R C Jain
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Citations

May 9, 2014·IEEE Transactions on Neural Networks and Learning Systems·Yang-Yin LinChin-Teng Lin
May 9, 2014·IEEE Transactions on Neural Networks and Learning Systems·Mingli Song, Witold Pedrycz
Feb 7, 2007·IEEE Transactions on Neural Networks·Abhijeet V Nandedkar, Prabir K Biswas
Mar 11, 2006·IEEE Transactions on Neural Networks·Jinn-Tsong TsaiTung-Kuan Liu
Mar 25, 2005·IEEE Transactions on Neural Networks·Dan Shen, Jose B Cruz

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