FCMAC-BYY: Fuzzy CMAC Using Bayesian Ying-Yang Learning

As an associative memory neural network model, the cerebellar model articulation controller (CMAC) has attractive properties of fast learning and simple computation, but its rigid structure makes it difficult to approximate certain functions. This research attempts to construct a novel neural fuzzy...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2006-10, Vol.36 (5), p.1180-1190
Main Authors: Minh Nhut Nguyen, Daming Shi, Quek, C.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Associative memory
Bayes Theorem
Bayesian analysis
Bayesian methods
Bayesian Ying-Yang (BYY) learning
Biomimetics - methods
Brain modeling
cerebellar model articulation controller (CMAC)
Cerebellum - physiology
CMAC
Computer networks
Computer Simulation
Cybernetics
Fuzzy
Fuzzy Logic
Fuzzy neural networks
fuzzy rule set
Fuzzy set theory
Fuzzy sets
Fuzzy systems
Humans
Learning
Logistic Models
Memory - physiology
Models, Neurological
Models, Statistical
Neural networks
Pattern Recognition, Automated - methods
Studies
truth-value restriction (TVR)
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Summary:As an associative memory neural network model, the cerebellar model articulation controller (CMAC) has attractive properties of fast learning and simple computation, but its rigid structure makes it difficult to approximate certain functions. This research attempts to construct a novel neural fuzzy CMAC, in which Bayesian Ying-Yang (BYY) learning is introduced to determine the optimal fuzzy sets, and a truth-value restriction inference scheme is subsequently employed to derive the truth values of the rule weights of implication rules. The BYY is motivated from the famous Chinese ancient Ying-Yang philosophy: everything in the universe can be viewed as a product of a constant conflict between opposites-Ying and Yang, a perfect status is reached when Ying and Yang achieve harmony. The proposed fuzzy CMAC (FCMAC)-BYY enjoys the following advantages. First, it has a higher generalization ability because the fuzzy rule sets are systematically optimized by BYY; second, it reduces the memory requirement of the network by a significant degree as compared to the original CMAC; and third, it provides an intuitive fuzzy logic reasoning and has clear semantic meanings. The experimental results on some benchmark datasets show that the proposed FCMAC-BYY outperforms the existing representative techniques in the research literature
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/TSMCB.2006.874691