A General Learning Scheme for CMAC-based Controller
Neural Processing Letters
Artificial Intelligence in Medicine
A nature inspired Ying-Yang approach for intelligent decision support in bank solvency analysis
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A novel associative memory approach to speech enhancement in a vehicular environment
Expert Systems with Applications: An International Journal
A novel self-organizing fuzzy rule-based system for modelling traffic flow behaviour
Expert Systems with Applications: An International Journal
GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms
Expert Systems with Applications: An International Journal
Hierarchical clustering for efficient memory allocation in CMAC neural network
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Supervised Pseudo Self-Evolving Cerebellar algorithm for generating fuzzy membership functions
Expert Systems with Applications: An International Journal
A Novel Fuzzy Associative Memory Architecture for Stock Market Prediction and Trading
International Journal of Fuzzy System Applications
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An improved modified cerebellar articulation controller (MCMAC) neural control algorithm with better learning and recall processes using momentum, neighborhood learning, and averaged trapezoidal output, is proposed in this paper. The learning and recall processes of MCMAC are investigated using the characteristic surface of MCMAC and the control action exerted in controlling a continuously variable transmission (CVT). Extensive experimental results demonstrate a significant improvement with reduced training time and an extended range of trained MCMAC cells. The improvement in recall process using the averaged trapezoidal output (MCMAC-ATO) are contrasted against the original MCMAC using the square of the Pearson product moment correlation coefficient. Experimental results show that the new recall process has significantly reduced the fluctuations in the control action of the MCMAC and addressed partially the problem associated with the resolution of the MCMAC memory array