Comparative study on the efficiency of hybrid learning procedures used for training of fuzzy-neural networks

Yancho Todorov, Margarita Terziyska

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Abstract

This chapter describes the development of a hybrid learning
procedure for a typical Fuzzy-Neural Network. In the classical case, the
learning of such kind of networks is being performed by the application of
the Gradient Descent. Due to its disadvantages to stuck in a local minima
and a slower convergence rate, implies the development of new learning
approaches in order to enable the possibility to be modeled more complex
dynamics. An alternative to be used is the second order gradient
approaches, based on Newton-like methods, since they provide a more
faster and efficient parameter learning. In the fuzzy-neural networks architectures has to be identified at each sampling period two groups of
parameters: the premise - the parameters of the fuzzy membership functions
and the consequent - the coefficients into the fuzzy rules. Since,
the first group of parameters is sensitive to application of faster learning
approaches, which may lead to model oscillations, a natural choice is to
develop a hybrid approach. It is proposed a two step learning algorithm,
based on minimization of an instant error function, which adjust the
fuzzy rule premise parameters by a Gradient descent approach, while the
rule consequents are scheduled either by Gauss-Newton and Levenberg-
Marquardt approaches. The efficency of the proposed approach is studied
through prediction by the proposed Fuzzy-Neural Network of two
common Chaotic Time Series: Rossler and Mackey-Glass.
Original languageEnglish
Title of host publicationMathematics in Industry
EditorsAngela Slavova
Pages246-257
Number of pages12
ISBN (Electronic)(13): 978-1-4438-6401-5
Publication statusPublished - 2013
MoE publication typeA3 Part of a book or another research book

Keywords

  • Fuzzy-neural networks
  • neural network modeling
  • neural network systems

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