Non-singleton interval type-2 fuzzy systems as integration methods in modular neural networks used genetic algorithms to design

Abstract

In this paper, we propose the use of Non-Singleton Interval Type-2 Fuzzy Systems (NSIT2FI) automatically designed through genetic algorithms as integration method of modular neural networks (MNN’s) for multimodal biometrics. The goal is to obtain such fuzzy systems as integrators, better recognition rate, and best mean square error in MNN. The results shown comparison between interval type-2 fuzzy systems and Non-singleton Type-2 Fuzzy Systems, where we can observe showing a significant difference that we can get higher recognition rate using non-singleton type-2 fuzzy logic.