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GA Based Fuzzy Neural Network Modeling for Nonlinear SISO System

In: DNA Computing Based Genetic Algorithm

Author

Listed:
  • Jili Tao

    (NingboTech University, School of Information Science and Engineering)

  • Ridong Zhang

    (Hangzhou Dianzi University, The Belt and Road Information Research Institute)

  • Yong Zhu

    (NingboTech University, School of Information Science and Engineering)

Abstract

Fuzzy neural networks are quite useful for nonlinear system identification with only input/output data information available. A fuzzy neural network and its improved framework are proposed and the improved genetic algorithms are designed for the structure and parameter optimization to catch the unknown plant dynamics. The hybrid encoding/decoding, neighborhood search operator and maintaining operator are presented to optimize the structure of the input layer, fuzzy rule layer and the parameters of the membership functions together. The liquid level and oxygen content modeling problems in the industrial coke furnace described in Ch. 6 are utilized to compare the performance of several methods. Simulation results show that GA optimized fuzzy neural network is superior in modeling precision and generalization capability. Fuzzy neural networks are quite useful for nonlinear system identification with only input/output data information available. A fuzzy neural network and its improved framework are proposed and the improved genetic algorithms are designed for the structure and parameter optimization to catch the unknown plant dynamics. The hybrid encoding/decoding, neighborhood search operator and maintaining operator are presented to optimize the structure of the input layer, fuzzy rule layer and the parameters of the membership functions together. The liquid level and oxygen content modeling problems in the industrial coke furnace described in Ch. 6 are utilized to compare the performance of several methods. Simulation results show that GA optimized fuzzy neural network is superior in modeling precision and generalization capability.

Suggested Citation

  • Jili Tao & Ridong Zhang & Yong Zhu, 2020. "GA Based Fuzzy Neural Network Modeling for Nonlinear SISO System," Springer Books, in: DNA Computing Based Genetic Algorithm, chapter 0, pages 167-191, Springer.
    • Handle: RePEc:spr:sprchp:978-981-15-5403-2_7
    DOI: 10.1007/978-981-15-5403-2_7
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