NDLI: A new fuzzy classifier with triangular membership functions

Content Provider	IEEE Xplore Digital Library
Author	Yong-Sheng Yang Chan, F.H.Y. Lam, F.K. Nguyen, H.
Copyright Year	1997
Description	Author affiliation: Dept. of Electr. & Electron. Eng., Hong Kong Univ., Hong Kong (Yong-Sheng Yang)
Abstract	Fuzzy logic is widely applied in control and modeling for its robustness, simplicity and clarity. It is also applied in classifier design with rules directly generated from numerical data. Some available rule generation methods, however, are either too complicated to implement or impractical for high dimensions. In this paper, we propose a new fuzzy classifier architecture. At the very beginning the training data is clustered at the input space. Fuzzy sets are then defined based on these clusters with triangular membership function. The outputs in the rule conclusion are initially determined by the "normalized vote" in the corresponding cluster. Fuzzy sets and conclusions can be adjusted through training. The proposed fuzzy system is simple in structure, and can be fast trained and easily implemented. Its classification performance is generally better than artificial neural network.
Starting Page	479
Ending Page	484
File Size	378311
Page Count	6
File Format	PDF
ISBN	0780341228
DOI	10.1109/ICNN.1997.611715
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-06-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy sets Fuzzy logic Training data Australia Robust control Fuzzy systems Artificial neural networks Large-scale systems Gaussian processes Microprocessors
Content Type	Text
Resource Type	Article

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