A Learning Method in Fuzzy Petri Nets Based on Particle Swarm Optimization Algorithm
Fuzzy Petri Nets(FPN) are a powerful modeling tool for knowledge-based systems based on fuzzy production rule. But the lack of learning mechanism is the weakness of Fuzzy Petri Nets. In this paper, a learning method in Fuzzy Petri Nets based on particle swarm optimization algorithm is proposed. Particle swarm optimization algorithm was proposed in 1995 by Kennedy and Eberhar, which requires only very originality math operators and has very fast operation speed. An application to the reliability estimate for bridge system is used as an illustrative example of this method. The result shows that this method is feasible, moreover this method can also be used to others complex systems reliability evaluation based on expert systems.
Fuzzy Petri net Expert system Particle swarm optimization.
YUAN Ju-mei WANG Ben-yi
Department of Electrical Engineering, College of North China Institute of Technology, Taiyuan Shanxi Department of Electronic Engineering, Zhejiang Industry and Trade Polytechnic, Wenzhou Zhejiang 3250
国际会议
北京
英文
2007-08-05（万方平台首次上网日期，不代表论文的发表时间）