Error-Compensated Marginal Linearization Method for Modeling a Fuzzy System
In this paper, a novel Error-compensated MArginal LINEarization (EMALINE) fuzzy modeling method is proposed. This method models a group of data information to a piecewise linear fuzzy system with high accuracy within a given error bound. It is proved that the fuzzy system generalized by the EMALINE method possesses universal approximation capability for a class of nonlinear systems. In addition, the theoretical approximation error bounds of the fuzzy system generalized by the EMALINE method are established and proved. Theoretical and practical results indicate that the EMALINE has better approximation accuracy than those of previous approaches. Numerical examples are shown to illustrate the validity of the proposed approach.