Identification of fuzzy model of non-uniformly sampled nonlinear systems based on singular value decomposition

WANG Hong-wei1,2 XIE Li-rong1

(1.School of Electrical Engineering, Xinjiang University, Urumqi 830036)
(2.School of Control Science and Control Engineering, Dalian University of Technology, Dalian 116024)

【Abstract】A modeling method based on matrix singular value decomposition (SVD) for model structure identification and parameter estimation is proposed for the nonlinear systems of complex, uncertain, and non-uniformly sampled data. Firstly, the matrix singular value decomposition method is used to analyze the relationship between the local model and the accumulation contribution rates, and the rule number of the fuzzy model is determined. Thus, the structure optimization of the model is realized. Then, in order to overcome the error accumulation and transfer of recursive least squares, this paper adopts the conclusion parameters of the recursive least squares estimation algorithm with singular value decomposition. Finally, a simulation example is given to illustrate the effectiveness of the proposed algorithm.

【Keywords】 nonuniformly sampled system; fuzzy model; singular value decomposition; recursive least squares; structure identification;

【DOI】

【Funds】 National Natural Science Foundation of China (61863034, 51667021) International S&T Cooperation Program of China (2013DFG61520) Science and Technology Aid Project in Xinjiang (2018E02072)

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This Article

ISSN:1001-0920

CN: 21-1124/TP

Vol 35, No. 03, Pages 757-762

March 2020

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Article Outline

Abstract

  • 0 Introduction
  • 1 Description of the fuzzy model
  • 2 Optimization of the antecedent structure of fuzzy model based on SVD
  • 3 Recursive identification parameters based on SVD
  • 4 Simulation example
  • 5 Conclusion
  • References