A New Transformed Input-Domain ANFIS for Highly Nonlinear System Modeling and Prediction

Elsaid Mohamed ABDELRAHIM; Takashi YAHAGI

A New Transformed Input-Domain ANFIS for Highly Nonlinear System Modeling and Prediction

Elsaid Mohamed ABDELRAHIM, Takashi YAHAGI

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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 of the adaptive neuro-fuzzy inference system (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 two frequently used benchmark problems shows that ANFIS with the uncorrelation process performs better than the original ANFIS under the same conditions.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E84-A No.8 pp.1981-1985

Publication Date: 2001/08/01

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Type of Manuscript: Special Section LETTER (Special Section on Digital Signal Processing)

Category: Nonlinear Signal Processing

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Elsaid Mohamed ABDELRAHIM, Takashi YAHAGI, "A New Transformed Input-Domain ANFIS for Highly Nonlinear System Modeling and Prediction" in IEICE TRANSACTIONS on Fundamentals, vol. E84-A, no. 8, pp. 1981-1985, August 2001, doi: .
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 of the adaptive neuro-fuzzy inference system (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 two frequently used benchmark problems shows that ANFIS with the uncorrelation process performs better than the original ANFIS under the same conditions.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_8_1981/_p

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@ARTICLE{e84-a_8_1981,
author={Elsaid Mohamed ABDELRAHIM, Takashi YAHAGI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A New Transformed Input-Domain ANFIS for Highly Nonlinear System Modeling and Prediction},
year={2001},
volume={E84-A},
number={8},
pages={1981-1985},
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 of the adaptive neuro-fuzzy inference system (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 two frequently used benchmark problems shows that ANFIS with the uncorrelation process performs better than the original ANFIS under the same conditions.},
keywords={},
doi={},
ISSN={},
month={August},}

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TY - JOUR
TI - A New Transformed Input-Domain ANFIS for Highly Nonlinear System Modeling and Prediction
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1981
EP - 1985
AU - Elsaid Mohamed ABDELRAHIM
AU - Takashi YAHAGI
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E84-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2001
AB - 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 of the adaptive neuro-fuzzy inference system (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 two frequently used benchmark problems shows that ANFIS with the uncorrelation process performs better than the original ANFIS under the same conditions.
ER -