A Design of Incremental Granular Model Using Context-Based Interval Type-2 Fuzzy C-Means Clustering Algorithm
Summary :
In this paper, a method for designing of Incremental Granular Model (IGM) based on integration of Linear Regression (LR) and Linguistic Model (LM) with the aid of fuzzy granulation is proposed. Here, IGM is designed by the use of information granulation realized via Context-based Interval Type-2 Fuzzy C-Means (CIT2FCM) clustering. This clustering approach are used not only to estimate the cluster centers by preserving the homogeneity between the clustered patterns from linguistic contexts produced in the output space, but also deal with the uncertainty associated with fuzzification factor. Furthermore, IGM is developed by construction of a LR as a global model, refine it through the local fuzzy if-then rules that capture more localized nonlinearities of the system by LM. The experimental results on two examples reveal that the proposed method shows a good performance in comparison with the previous works.
- Publication
- IEICE TRANSACTIONS on Information Vol.E99-D No.1 pp.309-312
- Publication Date
- 2016/01/01
- Publicized
- 2015/10/20
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2015EDL8076
- Type of Manuscript
- LETTER
- Category
- Biocybernetics, Neurocomputing
Authors
Keun-Chang KWAK
Chosun University
Keyword
Latest Issue
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Keun-Chang KWAK, "A Design of Incremental Granular Model Using Context-Based Interval Type-2 Fuzzy C-Means Clustering Algorithm" in IEICE TRANSACTIONS on Information,
vol. E99-D, no. 1, pp. 309-312, January 2016, doi: 10.1587/transinf.2015EDL8076.
Abstract: In this paper, a method for designing of Incremental Granular Model (IGM) based on integration of Linear Regression (LR) and Linguistic Model (LM) with the aid of fuzzy granulation is proposed. Here, IGM is designed by the use of information granulation realized via Context-based Interval Type-2 Fuzzy C-Means (CIT2FCM) clustering. This clustering approach are used not only to estimate the cluster centers by preserving the homogeneity between the clustered patterns from linguistic contexts produced in the output space, but also deal with the uncertainty associated with fuzzification factor. Furthermore, IGM is developed by construction of a LR as a global model, refine it through the local fuzzy if-then rules that capture more localized nonlinearities of the system by LM. The experimental results on two examples reveal that the proposed method shows a good performance in comparison with the previous works.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2015EDL8076/_p
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@ARTICLE{e99-d_1_309,
author={Keun-Chang KWAK, },
journal={IEICE TRANSACTIONS on Information},
title={A Design of Incremental Granular Model Using Context-Based Interval Type-2 Fuzzy C-Means Clustering Algorithm},
year={2016},
volume={E99-D},
number={1},
pages={309-312},
abstract={In this paper, a method for designing of Incremental Granular Model (IGM) based on integration of Linear Regression (LR) and Linguistic Model (LM) with the aid of fuzzy granulation is proposed. Here, IGM is designed by the use of information granulation realized via Context-based Interval Type-2 Fuzzy C-Means (CIT2FCM) clustering. This clustering approach are used not only to estimate the cluster centers by preserving the homogeneity between the clustered patterns from linguistic contexts produced in the output space, but also deal with the uncertainty associated with fuzzification factor. Furthermore, IGM is developed by construction of a LR as a global model, refine it through the local fuzzy if-then rules that capture more localized nonlinearities of the system by LM. The experimental results on two examples reveal that the proposed method shows a good performance in comparison with the previous works.},
keywords={},
doi={10.1587/transinf.2015EDL8076},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - A Design of Incremental Granular Model Using Context-Based Interval Type-2 Fuzzy C-Means Clustering Algorithm
T2 - IEICE TRANSACTIONS on Information
SP - 309
EP - 312
AU - Keun-Chang KWAK
PY - 2016
DO - 10.1587/transinf.2015EDL8076
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E99-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2016
AB - In this paper, a method for designing of Incremental Granular Model (IGM) based on integration of Linear Regression (LR) and Linguistic Model (LM) with the aid of fuzzy granulation is proposed. Here, IGM is designed by the use of information granulation realized via Context-based Interval Type-2 Fuzzy C-Means (CIT2FCM) clustering. This clustering approach are used not only to estimate the cluster centers by preserving the homogeneity between the clustered patterns from linguistic contexts produced in the output space, but also deal with the uncertainty associated with fuzzification factor. Furthermore, IGM is developed by construction of a LR as a global model, refine it through the local fuzzy if-then rules that capture more localized nonlinearities of the system by LM. The experimental results on two examples reveal that the proposed method shows a good performance in comparison with the previous works.
ER -
English
