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 article 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.
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