A novel neuro-fuzzy classification system design by a species-based hybrid algorithm

Abstract

In this paper, we propose a novel neuro-fuzzy classification system by a species-based hybrid of electromagnetism-like mechanism and back-propagation algorithms (SEMBP). The neuro-fuzzy classification system is constructed by an interval type-2 fuzzy neural system with asymmetric membership functions (AIT2FNS). The hybrid algorithm SEMBP combines the advantages of EM and BP algorithms. Three classification problems: the XOR data set, the breast cancer data set, and the Iris data set are used to illustrate the performance of our approach.