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Neurofuzzy state identification using prefiltering

Hong, X., Harris, C. J. and Wilson, P. A. (1999) Neurofuzzy state identification using prefiltering. IEE Proceedings-Control Theory and Applications, 146 (2). pp. 234-240. ISSN 1350-2379

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To link to this item DOI: 10.1049/ip-cta:19990121


A new state estimator algorithm is based on a neurofuzzy network and the Kalman filter algorithm. The major contribution of the paper is recognition of a bias problem in the parameter estimation of the state-space model and the introduction of a simple, effective prefiltering method to achieve unbiased parameter estimates in the state-space model, which will then be applied for state estimation using the Kalman filtering algorithm. Fundamental to this method is a simple prefiltering procedure using a nonlinear principal component analysis method based on the neurofuzzy basis set. This prefiltering can be performed without prior system structure knowledge. Numerical examples demonstrate the effectiveness of the new approach.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:18509
Uncontrolled Keywords:bias problem, neurofuzzy basis set, neurofuzzy network, neurofuzzy state identification, nonlinear principal component analysis method, prefiltering, state-space model, unbiased parameter estimates

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