Fuzzy rule-base driven orthogonal approximation
Articles > Article ยท 2007
Authors: Musa ALCI
Neural Computing and Applications, Journal Indexed in SCI Expanded, International, WoS, 2007, vol 17, pp 501-507
DOI: 10.1007/s00521-007-0146-2
Summary
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.