Response integration in Ensemble Neural Networks using interval type-2 Fuzzy logic

This paper describes a new approach for response integration in ensemble neural networks using interval type-2 fuzzy logic. When using ensemble neural networks it is important to choose a good method of response integration to obtain a better identification in pattern recognition. In this paper a comparative analysis between interval type-2 fuzzy logic, type-1 fuzzy logic and the Sugeno integral, as response integration methods, in ensemble neural networks is presented. Based on simulation results interval type-2 fuzzy logic is shown to be a superior method for response integration.