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。
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.
出处
《计算机系统应用》
2012年第5期201-204,共4页
Computer Systems & Applications
基金
云南省教育厅科研基金项目(2010Y060)
关键词
混沌
神经网络
微粒群算法
二阶微粒群算法
chaos
neural network
particle swarm optimization
two-order particle swarm optimization
