Paper Title:
A Compensatory Fuzzy Neural Network Modeling Method Based on Particle Swarm Clustering
  Abstract

According to modeling problem for complex systems, a compensatory fuzzy neural network (CFNN) modeling method based on particle swarm clustering is proposed: the particle swarm clustering is used to automatically separate the space of input-output data, obtain the numbers of inference rules of fuzzy model and find fuzzy rules. Based on the rules, we modified fuzzy reasoning process and established initial structure of compensatory fuzzy neural network. Then using adaptive rate algorithm optimized initial network parameters, which can obtain a faster training speed and more precision. Simulation results show that the proposed network has successfully modeled the oxidation decomposition reaction process.

  Info
Periodical
Edited by
Zhixiang Hou
Pages
5-8
DOI
10.4028/www.scientific.net/AMM.48-49.5
Citation
C. T. Man, T. F. Wang, X. B. Sun, X. X. Yang, J. Cui, "A Compensatory Fuzzy Neural Network Modeling Method Based on Particle Swarm Clustering", Applied Mechanics and Materials, Vols. 48-49, pp. 5-8, 2011
Online since
February 2011
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