Intelligent control using an Interval Type-2 Fuzzy Neural Network with a hybrid learning algorithm

Author

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

Author_Institution

Div. of Grad. Studies & Res., Baja California Autonomous Univ., Tijuana

fYear

2008

fDate

1-6 June 2008

Firstpage

893

Lastpage

900

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 fuzzy neural systems is as follows: it starts with the development of an rdquoInterval Type-2 Fuzzy Neuronrdquo, which is based on biological neural morphologies, followed by the 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 for the Takagi-Sugeno-Kang (TSK) type of reasoning.

Keywords

fuzzy neural nets; inference mechanisms; learning systems; neurocontrollers; IT2FNN architecture; Takagi-Sugeno-Kang reasoning; adaptive networks; biological neural morphology; fuzzy neural systems; hybrid learning algorithm; hybrid learning rule; intelligent control; interval type-2 fuzzy inference systems; interval type-2 fuzzy neural network; learning mechanisms; Adaptive systems; Biology computing; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Inference algorithms; Intelligent control; Learning systems; Morphology; Takagi-Sugeno-Kang model;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on

Conference_Location

Hong Kong

ISSN

1098-7584

Print_ISBN

978-1-4244-1818-3

Electronic_ISBN

1098-7584

Type

conf

DOI

10.1109/FUZZY.2008.4630476

Filename

4630476