›› 2014, Vol. 53 ›› Issue (5): 20-24.

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Research Based on D-FNN Algorithm on the Nonlinear Dynamic System Identification

YANG Wenyin1, ZHANG Defeng1, WANG Chuansheng2   

  1. 1. Department of Computer Science, Foshan University, Foshan 528000, China;
    2. Department of Computer Science, Jinan University, Guangzhou 510000, China
  • Online:2014-09-25 Published:2014-09-25

Abstract: Dynamic Fuzzy Neural Network (D-FNN), which basic idea is to construct a RBF neural network based on extension, could be seen as a TSK fuzzy system, as well as a Gaussian RBF neural network based on normalized. Within D-FNN algorithms, not only parameters could be adjusted in the learning process, but also the structure of fuzzy neural network could be automatically determined. Nonlinear parameters are directly decided by the training samples and Gaussian width, which only need one step training to achieve this goal. Due to the application of pruning strategies, network structure would not continue to grow, thus ensuring the generalization capability of the system. Simulations are performed on nonlinear dynamic system identification by using D-FNN, and the effectiveness and efficiency of D-FNN algorithm are proved by comparison with related algorithms.

Key words: D-FNN, fuzzy rule, system identification, Radial Basis Function

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