Variable selection algorithm for the construction of MIMO operating point dependent neurofuzzy networks

Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298 and Harris, C. J. (2001) Variable selection algorithm for the construction of MIMO operating point dependent neurofuzzy networks. IEEE Transactions on Fuzzy Systems, 9 (1). pp. 88-101. ISSN 1063-6706 doi: 10.1109/91.917117

Abstract/Summary

An input variable selection procedure is introduced for the identification and construction of multi-input multi-output (MIMO) neurofuzzy operating point dependent models. The algorithm is an extension of a forward modified Gram-Schmidt orthogonal least squares procedure for a linear model structure which is modified to accommodate nonlinear system modeling by incorporating piecewise locally linear model fitting. The proposed input nodes selection procedure effectively tackles the problem of the curse of dimensionality associated with lattice-based modeling algorithms such as radial basis function neurofuzzy networks, enabling the resulting neurofuzzy operating point dependent model to be widely applied in control and estimation. Some numerical examples are given to demonstrate the effectiveness of the proposed construction algorithm.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/18503
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher	IEEE
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