二阶微粒群优化神经网络的混沌系统辨识方法

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 learningrncan be seen as a function optimization problem and the neural network model based on two order particle swarmrnoptimization is proposed. Then, chaotic system is identified by BP trained with two-order PSO and the efficiency of BPrntrained with two-order PSO is compared with those of BP and RBF based on the identification of chaotic system. Thernexperimental results show that BP trained with two-order PSO is better than BP and RBF used in chaotic systemrnidentification.