非线性、大滞后系统神经网络辨识研究

基于神经网络的非线性、大滞后系统辨识是当前研究的热点之一,介绍了神经网络辨识的基本原理,研究了BP与RBF神经网络两种典型网络的设计和算法,最后通过MATLAB进行了仿真分析与比较。仿真结果表明:一致性方面RBF优于BP神经网络,RBF神经网络收敛速度更快,辨识效果更好;泛化性能方面RBF网络较差,不如BP网络。由此得出两种网络各自的优缺点,在实际应用中可以此作为神经网络模型辨识的参考。

Neural Network Identification of Nonlinear Macrohysteretic System

Nonlinear Macrohysteretic System identification based on neural network is one of the hotspots in current study. In this paper, the principle of neural network identification is introduced. By studying the design and algorithms of BP and RBF neural network, analysis and comparison is done with the simulation of MATLAB. The simulation results show that: RBF neural network, with faster convergence speed and higher accuracy, is better than BP on conformance, while BP neural network is better than RBF on generalization performance. According to the characteristics of the two networks, this conclusion can be used as a reference of the neural network model identification.