A nature inspired Ying-Yang approach for intelligent decision support in bank solvency analysis
Expert Systems with Applications: An International Journal
Stock Prediction Using FCMAC-BYY
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
An online Bayesian Ying-Yang learning applied to fuzzy CMAC
Neurocomputing
HebbR2-Taffic: A novel application of neuro-fuzzy network for visual based traffic monitoring system
Expert Systems with Applications: An International Journal
Online FCMAC-BYY Model with Sliding Window
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Fuzzy CMAC with incremental Bayesian Ying-Yang learning and dynamic rule construction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Efficient advertisement discovery for audio podcast content using candidate segmentation
EURASIP Journal on Audio, Speech, and Music Processing
Evolutionary FCMAC-BYY applied to stream data analysis
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
RFCMAC: A novel reduced localized neuro-fuzzy system approach to knowledge extraction
Expert Systems with Applications: An International Journal
Cultural dependency analysis for understanding speech emotion
Expert Systems with Applications: An International Journal
A novel brain-inspired neuro-fuzzy hybrid system for artificial ventilation modeling
Expert Systems with Applications: An International Journal
KCMAC-BYY: Kernel CMAC using Bayesian Ying-Yang learning
Neurocomputing
A Novel Fuzzy Associative Memory Architecture for Stock Market Prediction and Trading
International Journal of Fuzzy System Applications
Gait Pattern Based on CMAC Neural Network for Robotic Applications
Neural Processing Letters
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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