Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30012
Title: Knowledge-leverage-based TSK fuzzy system modeling
Authors: Deng, Z
Jiang, Y
Choi, KS 
Chung, FL 
Wang, S
Keywords: Fuzzy modeling
Fuzzy systems (FS)
Knowledge leverage (KL)
Missing data
Transfer learning
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks and learning systems, 2013, v. 24, no. 8, 6502723, p. 1200-1212 How to cite?
Journal: IEEE transactions on neural networks and learning systems 
Abstract: Classical fuzzy system modeling methods consider only the current scene where the training data are assumed to be fully collectable. However, if the data available from the current scene are insufficient, the fuzzy systems trained by using the incomplete datasets will suffer from weak generalization capability for the prediction in the scene. In order to overcome this problem, a knowledge-leverage-based fuzzy system (KL-FS) is studied in this paper from the perspective of transfer learning. The KL-FS intends to not only make full use of the data from the current scene in the learning procedure, but also effectively leverage the existing knowledge from the reference scenes. Specifically, a knowledge-leverage-based Takagi-Sugeno-Kang-type Fuzzy System (KL-TSK-FS) is proposed by integrating the corresponding knowledge-leverage mechanism. The new fuzzy system modeling technique is evaluated through experiments on synthetic and real-world datasets. The results demonstrate that KL-TSK-FS has better performance and adaptability than the traditional fuzzy modeling methods in scenes with insufficient data.
URI: http://hdl.handle.net/10397/30012
ISSN: 2162-237X
EISSN: 2162-2388
DOI: 10.1109/TNNLS.2013.2253617
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