Full Factorial Design Based ANFIS Model for Complex Systems

Author

Buragohain, M. ; Mahanta, C.

Author_Institution

Dept. of Electron. & Commun. Eng., Indian Inst. of Technol., Guwahati

fYear

2006

fDate

Sept. 2006

Firstpage

1

Lastpage

6

Abstract

In this paper we propose an adaptive network based fuzzy inference system (ANFIS) model where the number of data pairs employed for training is minimized by application of an engineering statistical technique called full factorial design. Our proposed method is experimentally validated by applying it to the benchmark Box and Jenkins gas furnace data. By employing our proposed method the number of data required for learning in the ANFIS network could be significantly reduced as compared to the number of data required for the conventional ANFIS method. The results obtained by applying our proposed method are compared with those obtained by using conventional ANFIS network. It was found that our model compares favourably well with conventional ANFIS model

Keywords

adaptive systems; fuzzy neural nets; inference mechanisms; learning (artificial intelligence); statistical analysis; ANFIS; adaptive network based fuzzy inference system model; complex system; engineering statistical technique; full factorial design; gas furnace data; Adaptive systems; Artificial neural networks; Data engineering; Design engineering; Furnaces; Fuzzy neural networks; Fuzzy systems; Large-scale systems; Neural networks; Uncertainty; ANFIS; Full Factorial Design (FFD); complex systems; subtractive clustering;

fLanguage

English

Publisher

ieee

Conference_Titel

India Conference, 2006 Annual IEEE

Conference_Location

New Delhi

Print_ISBN

1-4244-0369-3

Electronic_ISBN

1-4244-0370-7

Type

conf

DOI

10.1109/INDCON.2006.302803

Filename

4086274