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Identification of Neurofuzzy models using GTLS parameter estimation.

Authors
  • Jakubek, Stefan
  • Hametner, Christoph
Type
Published Article
Journal
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Oct 01, 2009
Volume
39
Issue
5
Pages
1121–1133
Identifiers
DOI: 10.1109/TSMCB.2009.2013132
PMID: 19336320
Source
Medline
License
Unknown

Abstract

In this paper, nonlinear system identification utilizing generalized total least squares (GTLS) methodologies in neurofuzzy systems is addressed. The problem involved with the estimation of the local model parameters of neurofuzzy networks is the presence of noise in measured data. When some or all input channels are subject to noise, the GTLS algorithm yields consistent parameter estimates. In addition to the estimation of the parameters, the main challenge in the design of these local model networks is the determination of the region of validity for the local models. The method presented in this paper is based on an expectation-maximization algorithm that uses a residual from the GTLS parameter estimation for proper partitioning. The performance of the resulting nonlinear model with local parameters estimated by weighted GTLS is a product both of the parameter estimation itself and the associated residual used for the partitioning process. The applicability and benefits of the proposed algorithm are demonstrated by means of illustrative examples and an automotive application.

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