Partitioning of Linearly Transformed Input Space in Adaptive Network Based Fuzzy Inference System
Summary :
This paper presents a new effective partitioning technique of linearly transformed input space in Adaptive Network based Fuzzy Inference System (ANFIS). The ANFIS is the fuzzy system with a hybrid parameter learning method, which is composed of a gradient and a least square method. The input space can be partitioned flexibly using new modeling inputs, which are the weighted linear combination of the original inputs by the proposed input partitioning technique, thus, the parameter learning time and the modeling error of ANFIS can be reduced. The simulation result illustrates the effectiveness of the proposed technique.
- Publication
- IEICE TRANSACTIONS on Information Vol.E84-D No.1 pp.213-216
- Publication Date
- 2001/01/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- LETTER
- Category
- Welfare Engineering
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Jeyoung RYU, Sangchul WON, "Partitioning of Linearly Transformed Input Space in Adaptive Network Based Fuzzy Inference System" in IEICE TRANSACTIONS on Information,
vol. E84-D, no. 1, pp. 213-216, January 2001, doi: .
Abstract: This paper presents a new effective partitioning technique of linearly transformed input space in Adaptive Network based Fuzzy Inference System (ANFIS). The ANFIS is the fuzzy system with a hybrid parameter learning method, which is composed of a gradient and a least square method. The input space can be partitioned flexibly using new modeling inputs, which are the weighted linear combination of the original inputs by the proposed input partitioning technique, thus, the parameter learning time and the modeling error of ANFIS can be reduced. The simulation result illustrates the effectiveness of the proposed technique.
URL: https://global.ieice.org/en_transactions/information/10.1587/e84-d_1_213/_p
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@ARTICLE{e84-d_1_213,
author={Jeyoung RYU, Sangchul WON, },
journal={IEICE TRANSACTIONS on Information},
title={Partitioning of Linearly Transformed Input Space in Adaptive Network Based Fuzzy Inference System},
year={2001},
volume={E84-D},
number={1},
pages={213-216},
abstract={This paper presents a new effective partitioning technique of linearly transformed input space in Adaptive Network based Fuzzy Inference System (ANFIS). The ANFIS is the fuzzy system with a hybrid parameter learning method, which is composed of a gradient and a least square method. The input space can be partitioned flexibly using new modeling inputs, which are the weighted linear combination of the original inputs by the proposed input partitioning technique, thus, the parameter learning time and the modeling error of ANFIS can be reduced. The simulation result illustrates the effectiveness of the proposed technique.},
keywords={},
doi={},
ISSN={},
month={January},}
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TY - JOUR
TI - Partitioning of Linearly Transformed Input Space in Adaptive Network Based Fuzzy Inference System
T2 - IEICE TRANSACTIONS on Information
SP - 213
EP - 216
AU - Jeyoung RYU
AU - Sangchul WON
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E84-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2001
AB - This paper presents a new effective partitioning technique of linearly transformed input space in Adaptive Network based Fuzzy Inference System (ANFIS). The ANFIS is the fuzzy system with a hybrid parameter learning method, which is composed of a gradient and a least square method. The input space can be partitioned flexibly using new modeling inputs, which are the weighted linear combination of the original inputs by the proposed input partitioning technique, thus, the parameter learning time and the modeling error of ANFIS can be reduced. The simulation result illustrates the effectiveness of the proposed technique.
ER -
English
