A Hybrid ε-Insensitive Learning of Fuzzy Systems

Abstract

Initially, it is shown that e-insensitive learning of a fuzzy system may be presented as a combination of both an e-insensitive gradient method and solving a system of linear inequalities. Then, a hybrid learning algorithm is introduced. Example is given of using this algorithm for design a fuzzy model of real ECG data. Simulation results show an improvement in the generalization ability of a fuzzy system learned by the new method with respect to the traditional and other e-insensitive learning methods.