二阶微粒群优化神经网络的混沌系统辨识方法

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。

Chaotic System Identification Based on BP Neural Network of Two Order Particle Swarm Optimization

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.