关键词: RBF神经网络, 粒子群优化算法, 故障诊断, 连续搅拌反应釜

Abstract:

A new PSO algorithm with dynamically changing inertia weight and study factors based on improved adaptive PSO was proposed,where the inertia weight of the particle was adjusted adaptively based on fitness of the particle.The diversity of inertia weight made a compromise between the global convergence and local convergence speed,so it can alleviate the problem of premature convergence effectively.The algorithm was applied to train RBF neural network and a model of fault diagnosis for CSTR was established,compared with PSO algorithm,the proposed algorithm can improve the training efficiency of neural network effectively and obtain good diagnosis results. 

Key words: RBF neural networks, particle swarm optimization(PSO), fault diagnosis, continuous stirred tank reactor(CSTR)