Hybrid Learning Algorithm for Interval Type-2 Fuzzy Neural Networks

Author

Castro, Juan R. ; Castillo, Oscar ; Melin, Patricia ; Rodríguez-Díaz, Antonio

Author_Institution

UABC Univ., Tijuana

fYear

2007

fDate

2-4 Nov. 2007

Firstpage

157

Lastpage

157

Abstract

In this paper, a class of interval type-2 fuzzy neural networks (IT2FNN) is proposed, which is functionally equivalent to interval type-2 fuzzy inference systems. The computational process envisioned for a fuzzy-neural system is as follows: it starts with the development of an "interval type-2 fuzzy neuron", which is based on biological neural morphologies, followed by learning mechanisms. We describe how to decompose the parameter set such that the hybrid learning rule of adaptive networks can be applied to the IT2FNN architecture.

Keywords

adaptive systems; fuzzy logic; fuzzy neural nets; genetic algorithms; learning (artificial intelligence); adaptive networks; biological neural morphologies; computational process; fuzzy-neural system; hybrid learning algorithm; hybrid learning rule; interval type-2 fuzzy neural networks; interval type-2 fuzzy neuron; learning mechanisms; Biology computing; Computational intelligence; Computer networks; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Hybrid intelligent systems; Neural networks; Neurons; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Granular Computing, 2007. GRC 2007. IEEE International Conference on

Conference_Location

Fremont, CA

Print_ISBN

978-0-7695-3032-1

Type

conf

DOI

10.1109/GrC.2007.116

Filename

4403086