Paper Title:
Reinforcement Learning Based Self-Constructing Fuzzy Neural Network Controller for AC Motor Drives
  Abstract

A self-constructing fuzzy neural network (SCFNN) based on reinforcement learning is proposed in this study. In the SCFNN, structure and parameter learning are implemented simultaneously. Structure learning is based on uniform division of the input space and distribution of membership function. The structure and membership parameters are organized as real value chromosomes, and the chromosomes are trained by the reinforcement learning based on genetic algorithm. This paper uses Matlab/Simulink to establish simulation platform and several simulations are provided to demonstrate the effectiveness of the proposed SCFNN control stratagem with the implementation of AC motor speed drive. The simulation results show that the AC drive system with SCFNN has good anti-disturbance performance while the load change randomly.

  Info
Periodical
Advanced Materials Research (Volumes 139-141)
Edited by
Liangchi Zhang, Chunliang Zhang and Tielin Shi
Pages
1763-1768
DOI
10.4028/www.scientific.net/AMR.139-141.1763
Citation
Q. Wang, J. Y. Qin, J. H. Zhou, "Reinforcement Learning Based Self-Constructing Fuzzy Neural Network Controller for AC Motor Drives", Advanced Materials Research, Vols. 139-141, pp. 1763-1768, 2010
Online since
October 2010
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Price
$35.00
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