Fuzzy rule-base driven orthogonal approximation


Alci M.

NEURAL COMPUTING & APPLICATIONS, vol.17, pp.501-507, 2008 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 17
  • Publication Date: 2008
  • Doi Number: 10.1007/s00521-007-0146-2
  • Title of Journal : NEURAL COMPUTING & APPLICATIONS
  • Page Numbers: pp.501-507

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.