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Deterministic Annealing Integrated with ε-Insensitive Learning in Neuro-fuzzy Systems

  • Robert Czabański
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)

Abstract

In this paper a new method of parameters estimation for neuro-fuzzy system with parameterized consequents is presented. The novelty of the learning algorithm consists of an application of the deterministic annealing method integrated with ε-insensitive learning. This method allows to improve neuro-fuzzy modeling quality in the sense of an increase in generalization ability and outliers robustness. To demonstrate performance of the proposed procedure two numerical experiments concerning benchmark problems of prediction and identification are given.

Keywords

Root Mean Square Error Fuzzy System Radial Basis Function Neural Network Generalization Ability Parameterized Consequents 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Robert Czabański
    • 1
  1. 1.Institute of ElectronicsSilesian University of TechnologyGliwicePoland

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