A new transformed input-domain ANFIS for highly nonlinear system modeling and prediction

Author

Abdelrahi, Elsaid Mohamed ; Yahagi, Takashi

Author_Institution

Graduate Sch. of Sci. & Technol., Chiba Univ., Japan

Volume

1

fYear

2001

fDate

2001

Firstpage

655

Abstract

In two or more-dimensional systems where the components of the sample data are strongly correlated, it is not proper to divide the input space into several subspaces without considering the correlation. In this paper, we propose the usage of the method of principal component in order to uncorrelate and remove any redundancy from the input space or the adaptive-neuro fuzzy inference systems (ANFIS). This leads to an effective partition of the input space to the fuzzy model and significantly reduces the modeling error. A computer simulation for three frequently used benchmark problems shows that ANFIS with the uncorrelation process performs better than the original ANFIS

Keywords

Karhunen-Loeve transforms; adaptive systems; correlation theory; feedforward neural nets; fuzzy neural nets; inference mechanisms; learning (artificial intelligence); nonlinear control systems; prediction theory; principal component analysis; redundancy; sampling methods; adaptive-neuro fuzzy inference systems; correlation; frequently used benchmark problems; fuzzy model; highly nonlinear system modeling; input space; many-dimensional systems; modeling error; prediction; principal component; redundancy; sample data; subspaces; transformed input-domain ANFIS; two-dimensional systems; uncorrelation process; Adaptive systems; Discrete transforms; Fuzzy logic; Fuzzy systems; Nonlinear systems; Partitioning algorithms; Predictive models; Principal component analysis; Redundancy; Space technology;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Computer Engineering, 2001. Canadian Conference on

Conference_Location

Toronto, Ont.

ISSN

0840-7789

Print_ISBN

0-7803-6715-4

Type

conf

DOI

10.1109/CCECE.2001.933761

Filename

933761