基于支持向量机的可分离非线性动态系统辨识  

Identification of Separable Variable Nonlinear Dynamical System Based on SVMs

作　　者：张莉[1] 席裕庚[1] 

机构地区：[1]上海交通大学自动化研究所,上海200030

出　　处：《自动化学报》2005年第6期965-969,共5页Acta Automatica Sinica

基　　金：上海市科委项目(04DZ11008)

摘　　要：针对状态变量和控制变量可分离的非线性动态系统模型,通过引入两个非线性核函数重新设计了标准支持向量机的回归估计模型,使之适用于非线性动态系统的辨识.它包含两个分别关于状态变量和控制变量的非线性函数,用于辨识可分离变量非线性动态系统中的两个非线性函数.文中的仿真实验验证了我们算法用于非线性动态系统辨识的有效性.In this paper, for the case that the state variables can be separated from the control variables in a nonlinear dynamic system, an improved regression estimation algorithm based on SVMs is presented and then applied to nonlinear system identification. This kind of learning machines includes two nonlinear functions whose variables are the state and the control ones, respectively, and they are used to identify the two nonlinear functions in the separable-variable nonlinear dynamic system. The simulation results validate the efficiency of our method.

关 键 词：系统辨识 支持向量机 回归估计 核函数 

分 类 号：N945.14[自然科学总论—系统科学]