基于神经网络的非线性系统近似线性化(英文)  

Approximate Linearization of Nonlinear Systems A Neural Network Approach

作　　者：裴海龙[1] 周其节[1] 

机构地区：[1]华南理工大学自动控制工程系,广州510641

出　　处：《控制理论与应用》1998年第1期31-38,共8页Control Theory & Applications

摘　　要：神经网络具有同时逼近某一函数及其高阶导数的功能，这一结果为神经网络在非线性系统中的应用提供了可行的工具．本文提出了一种利用网络近似功能的非线性系统的近似线性化方法，无论系统是否满足可积条件，神经网络都可实现其对合条件的近似积分，从而构造满足系统近似线性化的反馈控制．对球－杆系统的仿真结果显示了这种方法的有效性．Recent researches show that neural networks have the ablilty to approximate a function as well as its derivatives. This result offers a promising opportunity to introduce neural network theory into nonlinear system control. In this paper a novel method of approximate nonlinear system linearization with neural networks is proposed. The network approximator is designed to integrate the involutive equation of a nonlinear system whether the integrability condition is satisfied or not. Simulation results show that this method is feasible.

关 键 词：神经网络 近似线性化 非线性系统 

分 类 号：TP271[自动化与计算机技术—检测技术与自动化装置]