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Evolutionary Optimization of Union-Based Rule-Antecedent Fuzzy Neural Networks and Its Applications

  • Chang-Wook Han
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)

Abstract

A union-based rule-antecedent fuzzy neural networks (URFNN), which can guarantee a parsimonious knowledge base with reduced number of rules, is proposed. The URFNN allows union operation of input fuzzy sets in the antecedents to cover bigger input domain compared with the complete structure rule which consists of AND combination of all input variables in its premise. To construct the URFNN, we consider the union-based logic processor (ULP) which consists of OR and AND fuzzy neurons. The fuzzy neurons exhibit learning abilities as they come with a collection of adjustable connection weights. In the development stage, genetic algorithm (GA) constructs a Boolean skeleton of URFNN, while gradient-based learning refines the binary connections of GA-optimized URFNN for further improvement of the performance index. A cart-pole system is considered to verify the effectiveness of the proposed method.

Keywords

Genetic Algorithm Rule Base Evolutionary Optimization Structure Rule Union Operation 
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 2008

Authors and Affiliations

  • Chang-Wook Han
    • 1
  1. 1.Department of Electrical EngineeringDong-Eui UniversityBusanSouth Korea

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