A training algorithm of process neural networks based on CGA combined with PSO

CORC > 北京大学 > 信息科学技术学院

	A training algorithm of process neural networks based on CGA combined with PSO
	Xu, Shao-Hua ; He, Xin-Gui
刊名	kongzhi yu juececontrol and decision
	2013
英文摘要	Aiming at the training problem of time-varying input-output process neural networks (PNN), a learning algorithm based on chaos genetic algorithm (CGA) combined with particle swarm optimization (PSO) whose inertial factor is dynamic is proposed in the paper. With the application of the experience memory and sharing information of PSO algorithm, and chaos track traverse searching of CGA, the hybrid evolutionary optimization mechanism of CGA and PSO algorithm is built based on the PNN's training objective function. The adaptive switching of two algorithms is implemented through estimating the fitness and optimization efficiency, and the global optimal solution is obtained in feasible solution space. Experimental results show that the algorithm considerably improves the training efficiency of PNN.; EI; 0; 9; 1393-1398; 28
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/294324]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Xu, Shao-Hua,He, Xin-Gui. A training algorithm of process neural networks based on CGA combined with PSO[J]. kongzhi yu juececontrol and decision,2013.
APA	Xu, Shao-Hua,&He, Xin-Gui.(2013).A training algorithm of process neural networks based on CGA combined with PSO.kongzhi yu juececontrol and decision.
MLA	Xu, Shao-Hua,et al."A training algorithm of process neural networks based on CGA combined with PSO".kongzhi yu juececontrol and decision (2013).