哈尔滨工业大学学报（英文版）2007，Vol.14Issue(3)：316-321,6.
A fuzzy neural network evolved by particle swarm optimization
A fuzzy neural network evolved by particle swarm optimization
摘要
Abstract
A cooperative system of a fuzzy logic model and a fuzzy neural network (CSFLMFNN) is proposed,in which a fuzzy logic model is acquired from domain experts and a fuzzy neural network is generated and prewired according to the model. Then PSO-CSFLMFNN is constructed by introducing particle swarm optimization (PSO) into the cooperative system instead of the commonly used evolutionary algorithms to evolve the prewired fuzzy neural network. The evolutionary fuzzy neural network implements accuracy fuzzy inference without rule matching. PSO-CSFLMFNN is applied to the intelligent fault diagnosis for a petrochemical engineering equipment, in which the cooperative system is proved to be effective. It is shown by the applied results that the performance of the evolutionary fuzzy neural network outperforms remarkably that of the one evolved by genetic algorithm in the convergence rate and the generalization precision.关键词
fuzzy neural network/evolving/particle swarm optimization/intelligent fault diagnosisKey words
fuzzy neural network/evolving/particle swarm optimization/intelligent fault diagnosis分类
信息技术与安全科学引用本文复制引用
PENG Zhi-ping,PENG Hong..A fuzzy neural network evolved by particle swarm optimization[J].哈尔滨工业大学学报（英文版）,2007,14(3):316-321,6.基金项目
Sponsored by the Natural Science Foundation of Guangdong Province of China( Grant No. 06029281 and 05011905 ). （ Grant No. 06029281 and 05011905 ）
