具备视角协同学习能力的多视角TSK型模糊系统

doi:10.11999/JEIT151209

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Abstract

Conventional fuzzy system modeling methods essentially belong to the single-view learning modality. In multi-view-oriented data scenarios, they can only cope with each view separately, which is prone to incurring their unsatisfactory generalization performance. In response to such problem, the fuzzy system modeling method with the ability of multi-view learning is pursued. To this end, based on the classic L2 norm Takagi-Sugeno-Kang (TSK) fuzzy system, by means of the collaborative learning items qualified for multi-view learning, the core Multi-View TSK Fuzzy System (MV-TSK-FS) modeling method is presented. MV-TSK-FS can not only effectively utilize the independent components composed of the characteristics affiliated to each view, but also take full advantage of the potential information occurred by the interrelated effects among views, which eventually facilitates its relatively strong generalization ability. The experimental results performed on both synthetic and real-life datasets indicate that, compared with some traditional single-view methods, this propounded multi-view fuzzy modeling system owns preferable applicability as well as generalization.

Key words： Multi-view learning Collaborative learning Fuzzy modeling Takagi-Sugeno-Kang (TSK) type fuzzy system

Received: 29 October 2015 Published: 05 May 2016

PACS:

TP391

Fund:

The National Natural Science Foundation of China (61300151), The Natural Science Foundation of Jiangsu Province (BK20130155), The R&D Frontier Grant of Jiangsu Province (BY2013015-02), The Fundamental Research Funds for the Central Universities (JUSRP51614A)

Corresponding Authors: JIANG Yizhang E-mail: jyz0512@163.com

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	Articles by authors
	CHENG Yang
	GU Xiaoqing
	JIANG Yizhang
	HANG Wenlong
	QIAN Pengjiang
	WANG Shitong

Cite this article:

CHENG Yang,GU Xiaoqing,JIANG Yizhang等. Multi-view TSK Fuzzy System via Collaborative Learning[J]. JEIT, 2016, 38(8): 2054-2061.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT151209 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I8/2054

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