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Received:September 06, 2011 Revised:October 09, 2011
Received:September 06, 2011 Revised:October 09, 2011
中文摘要: 针对BP 神经网络在学习算法中的不足,将BP 神经网络的权值和阀值训练问题转换为优化问题,提出一种利用二阶微粒群算法优化的神经网络的算法。其次,运用基于二阶微粒群算法训练的神经网络模型对混沌系统进行辨识,并与传统的BP 神经网络、RBF 网络对同一混沌系统辨识的结果进行比较。实验表明,利用二阶微粒群优化算法训练神经网络进行混沌系统辨识,辨识的效果优于其它几种神经网络模型,可有效用于混沌系统的辨识。
Abstract:Aiming to the shortage of BP neural network in training algorithm, the problem of neural network learning can be seen as a function optimization problem and the neural network model based on two order particle swarm optimization is proposed. Then, chaotic system is identified by BP trained with two-order PSO and the efficiency of BP trained with two-order PSO is compared with those of BP and RBF based on the identification of chaotic system. The experimental results show that BP trained with two-order PSO is better than BP and RBF used in chaotic system identification.
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基金项目:云南省教育厅科研基金项目(2010Y060)
| Author Name | Affiliation |
| ZHANG Kun | Department of Mathematics, Chuxiong Normal University, Chuxiong 675000, China |
| LIANG Lin | Department of Mathematics, Chuxiong Normal University, Chuxiong 675000, China |
| Author Name | Affiliation |
| ZHANG Kun | Department of Mathematics, Chuxiong Normal University, Chuxiong 675000, China |
| LIANG Lin | Department of Mathematics, Chuxiong Normal University, Chuxiong 675000, China |
引用文本：
张坤,梁林.二阶微粒群优化神经网络的混沌系统辨识方法.计算机系统应用,2012,21(5):201-204
ZHANG Kun,LIANG Lin.Chaotic System Identification Based on BP Neural Network of Two Order Particle Swarm Optimization.COMPUTER SYSTEMS APPLICATIONS,2012,21(5):201-204
张坤,梁林.二阶微粒群优化神经网络的混沌系统辨识方法.计算机系统应用,2012,21(5):201-204
ZHANG Kun,LIANG Lin.Chaotic System Identification Based on BP Neural Network of Two Order Particle Swarm Optimization.COMPUTER SYSTEMS APPLICATIONS,2012,21(5):201-204
