International Journal of Computational Intelligence Systems

Volume 11, Issue 1, 2018, Pages 737 - 756

Contribution-Factor based Fuzzy Min-Max Neural Network: Order-Dependent Clustering for Fuzzy System Identification

Authors
Peixin Hou1, houpeixin@tongji.edu.cn, Jiguang Yue1, yuejiguang@tongji.edu.cn, Hao Deng2, gavind@163.com, Shuguang Liu3, liusgliu@tongji.edu.cn, Qiang Sun1, 10qsun@tongji.edu.cn
1Department of Control Science and Engineering, Tongji University, Shanghai, China
2School of Physics and Electronics, Henan University, Kaifeng, China
3Department of Hydraulic Engineering, Tongji University, Shanghai, China
Received 19 May 2017, Accepted 3 March 2018, Available Online 19 March 2018.
DOI
https://doi.org/10.2991/ijcis.11.1.57How to use a DOI?
Keywords
fuzzy min-max neural network; clustering; adaptive resonance theory; contribution-factor; order-dependent; fuzzy inference system
Abstract

This study addresses the construction of Takagi-Sugeno-Kang (TSK) fuzzy models by means of clustering. A contribution-factor based fuzzy min-max neural network (CFMN) is developed based on Simpson’s well-known fuzzy min-max neural network (FMNN) for clustering. The contribution-factor (CF) is also known as the typical pattern, and the membership threshold above which a pattern can be a CF of a cluster can be specified by the user. The stability issue is addressed and unnecessary overlaps in FMNN can be avoided. Furthermore, two considerations are combined in the clustering process to fully exploit the information in the data: 1) patterns (points) are generated in a sequence, so it’s reasonable to capture the order-dependent information of data, and 2) the clustering process shouldn’t be influenced too much by noisy data or outliers. As a result, CFMN can put most cluster centers in high-density regions of clusters without influence of the low-density regions. This feature is very important when clusters are used as fuzzy rules because the high-density region of some cluster can be interpreted as the most common part of that rule. Simulations are performed to illustrate the clustering behavior of CFMN and identification performance of the resulting fuzzy inference system (CFMN-FIS). It is shown that the proposed algorithm is fast to learn and has good prediction performance.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
11 - 1
Pages
737 - 756
Publication Date
2018/03/19
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.11.1.57How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Peixin Hou
AU  - Jiguang Yue
AU  - Hao Deng
AU  - Shuguang Liu
AU  - Qiang Sun
PY  - 2018
DA  - 2018/03/19
TI  - Contribution-Factor based Fuzzy Min-Max Neural Network: Order-Dependent Clustering for Fuzzy System Identification
JO  - International Journal of Computational Intelligence Systems
SP  - 737
EP  - 756
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.57
DO  - https://doi.org/10.2991/ijcis.11.1.57
ID  - Hou2018
ER  -