基于外积FLNN的非线性系统辨识  

Nonlinear Dynamic System Identification Using Functional Link Artificial Neural Networks

作　　者：李萍[1] 吴乐南[1] 

机构地区：[1]东南大学无线电工程系

出　　处：《微计算机信息》2006年第02S期257-259,共3页Control & Automation

基　　金：国家自然科学基金资助项目(60472054)

摘　　要：函数型连接神经网络通过对输入模式预先进行非线性扩展,增强了输入信号的模式表达,从而大大简化网络结构,降低计算复杂度。本文提出一种外积扩展型连接神经网络,用于辨识幂函数非线性系统,并与MLP和CFLNN网络对比,仿真结果表明,外积型辨识幂函数非线性系统结构简单、计算量低、性能最优。A functional link neural network can expand its input pattern to eliminate the need of hidden layer without sacrifice its performance. Thus the network structure and the computational complexity can be remarkably reduced. In this paper, a muti-extended link neural network is introduced in identification of power function nonlinear system. It is contrasted with MLP and CFLNN network and simulation result indicates that its structure is very simple and its computational complexity is low.

关 键 词：外积扩展 函数型连接神经网络 MLP 非线性系统识别 

分 类 号：TN911[电子电信—通信与信息系统]