[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()

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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

Author(s):

Li Jun;  Liu Junhua

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

Keywords:

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

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.

References:

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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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