基于支持向量机的非线性系统辨识  

Support Vector Machine Based Nonlinear Systems Identification

作　　者：张浩然[1] 韩正之[1] 李昌刚[1] 

机构地区：[1]上海交通大学自动化系,上海200030

出　　处：《系统仿真学报》2003年第1期119-121,共3页Journal of System Simulation

摘　　要：支持向量机(SVM)是一种基于结构风险最小化原理的学习技术,也是一种新的具有很好泛化性能的回归方法,该文利用支持向量机对非线性系统进行黑箱建模,介绍了v-SVR的基本理论,并进行了仿真实验,结果验证了所提出的方法的正确性和有效性。Support vector machine is a learning technique based on the structural risk minimization principle, and it is also a class of regression method with good generalization ability. This paper uses support vector machine to model nonlinear dynamical systems, and briefly describes the theory of v-SVR. A simulation example is taken to demonstrate correctness and effectiveness of the proposed approach.

关 键 词：支持向量机 非线性系统 统计学习理论 回归估计 系统辨识 

分 类 号：O231.3[理学—运筹学与控制论]