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Fuzzy Neural Network Optimization by a Multi-Objective Differential Evolution Algorithm

  • Ming Ma
  • Li-biao Zhang
  • Xiang-li Xu
Part of the Advances in Soft Computing book series (AINSC, volume 54)

Abstract

Designing a set of fuzzy neural networks can be considered as solving a multi-objective optimization problem. An algorithm for solving the multi-objective optimization problem is presented based on differential evolution through the max-min distance density and a Pareto candidate solution set maintenance method. The search for the Pareto Optimal Set of fuzzy neural networks optimization problems is performed. Numerical simulations for taste identification of tea show that the algorithm is feasible and efficient.

Keywords

Differential evolution fuzzy neural network fuzzy rule 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ming Ma
    • 1
  • Li-biao Zhang
    • 2
  • Xiang-li Xu
    • 2
  1. 1.Information Manage CenterBeihua UniversityJilinP.R. China
  2. 2.College of Computer Science and TechnologyJilin University, Email: mam@mail.edu.cnChangchunP.R. China

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