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Fuzzy rule-base driven orthogonal approximation

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Abstract

In this study, orthogonal approximation concept is applied to fuzzy systems. We propose a new useful model adapted from the well-known Sugeno type fuzzy system. The proposed fuzzy model is a generalization of the zero-order Sugeno fuzzy system model. Instead of linear functions in standard Sugeno model, we use nonlinear functions in the consequent part. The nonlinear functions are selected from a trigonometric orthogonal basis. Orthogonal function parameters are trained along with the Sugeno fuzzy system. The proposed model is demonstrated using three simulations—a nonlinear piecewise-continuous scalar function modeling and filtering, nonlinear dynamic system identification, and time series prediction. Finally some performance comparisons are carried out.

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  1. Engineering Faculty, Department of Electrical and Electronics Engineering, Ege University, Bornova, Izmir, 35100, Turkey

    Musa Alci

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  1. Musa Alci
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Correspondence to Musa Alci.

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Alci, M. Fuzzy rule-base driven orthogonal approximation. Neural Comput & Applic 17, 501–507 (2008). https://doi.org/10.1007/s00521-007-0146-2

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  • DOI: https://doi.org/10.1007/s00521-007-0146-2

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