[1] Li Jun, Liu Junhua,. Identification of dynamic systemsusing support vector regression neural networks [J]. Journal of Southeast University (English Edition), 2006, 22 (2): 228-233. [doi:10.3969/j.issn.1003-7985.2006.02.018]

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Identification of dynamic systemsusing support vector regression neural networks()

基于支持向量回归神经网络的动态系统辨识

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

22

Issue:

2006 2

Page:

228-233

Research Field:

Automation

Publishing date:

2006-06-30

Info

Title:

Identification of dynamic systemsusing support vector regression neural networks

基于支持向量回归神经网络的动态系统辨识

Author(s):

Li Jun;  Liu Junhua

School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

李军;  刘君华

西安交通大学电气工程学院, 西安 710049

Keywords:

support vector regression;  neural network;  system identification;  robust learning algorithm;  adaptabi-lity

支持向量回归;  神经网络;  系统辨识;  鲁棒学习算法;  自适应性

PACS:

TP183

DOI:

10.3969/j.issn.1003-7985.2006.02.018

Abstract:

A novel adaptive support vector regression neural network(SVR-NN)is proposed, which combines respectively merits of support vector machines and a neural network.First, a support vector regression approach is applied to determine the initial structure and initial weights of the SVR-NN so that the network architecture is easily determined and the hidden nodes can adaptively be constructed based on support vectors.Furthermore, an annealing robust learning algorithm is presented to adjust these hidden node parameters as well as the weights of the SVR-NN.To test the validity of the proposed method, it is demonstrated that the adaptive SVR-NN can be used effectively for the identification of nonlinear dynamic systems.Simulation results show that the identification schemes based on the SVR-NN give considerably better performance and show faster learning in comparison to the previous neural network method.

结合支持向量机和神经网络各自的优点, 提出了一种新颖的自适应支持向量回归神经网络(SVR-NN).首先, 利用支持向量回归方法确定SVR-NN的初始结构和初始化权值, 基于支持向量自适应地构造SVR-NN神经网络的隐层节点;然后, 使用退火过程的鲁棒学习算法更新网络节点参数和权值.为了验证所提出方法的有效性, 给出了自适应SVR-NN应用于非线性动态系统辨识的实例.仿真结果表明, 与以前的神经网络方法相比, 基于SVR-NN网络的辨识方案能获得相当好的性能, 它具有很快的收敛速度.因此, 自适应的SVR-NN为非线性系统辨识提供了极有吸引力的新途径.

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Memo

Memo:

Biographies: Li Jun(1969—), male, graduate, lijun691201@yahoo.com.cn;Liu Junhua(1938—), female, professor, Junhliu@mail.xjtu.edu.cn.

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