A new BP network based on improved PSO algorithm and its application on fault diagnosis of gas turbine

被引：0
|
作者：
Hu, Wei [1 ,2 ]
Hu, Jingtao [1 ]
机构：
[1] Chinese Acad Sci, Shenyang Inst Automat, Dept Ind Control Syst, Shenyang 110016, Peoples R China
[2] Chinese Acad Sci, Grad Sch, Beijing 100039, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2007, PT 3, PROCEEDINGS | 2007年 / 4493卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Aiming at improving the convergence performance of conventional BP neural network, this paper presents an improved PSO algorithm instead of gradient descent method to optimize the weights and thresholds of BP network. The strategy of the algorithm is that in each iteration loop, on every dimension d of particle swarm containing n particles, choose the particle whose velocity decreases most quickly to mutate its velocity according to some probability. Simulation results show that the new algorithm is very effective. It is successful to apply the algorithm to gas turbine fault diagnosis.
引用
收藏
页码：277 / +
页数：2
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