NDLI: SaFIN: A Self-Adaptive Fuzzy Inference Network

Content Provider	IEEE Xplore Digital Library
Author	Sau Wai Tung Chai Quek Cuntai Guan
Copyright Year	1990
Abstract	There are generally two approaches to the design of a neural fuzzy system: (1) design by human experts, and (2) design through a self-organization of the numerical training data. While the former approach is highly subjective, the latter is commonly plagued by one or more of the following major problems: (1) an inconsistent rulebase; (2) the need for prior knowledge such as the number of clusters to be computed; (3) heuristically designed knowledge acquisition methodologies; and (4) the stability-plasticity tradeoff of the system. This paper presents a novel self-organizing neural fuzzy system, named Self-Adaptive Fuzzy Inference Network (SaFIN), to address the aforementioned deficiencies. The proposed SaFIN model employs a new clustering technique referred to as categorical learning-induced partitioning (CLIP), which draws inspiration from the behavioral category learning process demonstrated by humans. By employing the one-pass CLIP, SaFIN is able to incorporate new clusters in each input-output dimension when the existing clusters are not able to give a satisfactory representation of the incoming training data. This not only avoids the need for prior knowledge regarding the number of clusters needed for each input-output dimension, but also allows SaFIN the flexibility to incorporate new knowledge with old knowledge in the system. In addition, the self-automated rule formation mechanism proposed within SaFIN ensures that it obtains a consistent resultant rulebase. Subsequently, the proposed SaFIN model is employed in a series of benchmark simulations to demonstrate its efficiency as a self-organizing neural fuzzy system, and excellent performances have been achieved.
Sponsorship	IEEE Computational Intelligence Society
Page Count	13
File Size	671433
Starting Page	1928
Ending Page	1940
File Format	PDF
ISSN	10459227
Volume Number	22
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy systems Fuzzy neural networks Computational modeling Training data Learning systems self-organizing Categorical learning-induced partitioning fuzzy neural networks hybrid intelligent systems
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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