Abstract: Based on biological prototype of human brain and improved understanding of the functionality of the neurons and the pattern of their interconnections in the brain, a theoretical model used to explain the information-processing characteristics of the cerebellum was developed independently by Marr (1969) and Albus (1971). Cerebellar model articulation controller (CMAC) was first proposed by Albus in 1974. CMAC is a learning structure that imitates the organization and functionality of the cerebellum of the human brain. That model revealed the structure and functionality of the various cells and fibers in the cerebellum. The core of CMAC is an associative memory which has the ability to approach complex nonlinear functions. CMAC takes advantage of the input-redundancy by using distributed storage and can learn nonlinear functions extremely quickly due to the on-line adjustment of its system parameters. CMAC is classified as a non-fully connected perceptron-like associative memory network with overlapping receptive-fields. It has good generalization capability and fast learning property and is suitable for a lot of applications. This speech introduces several CMAC-based adaptive learning systems; these systems combine the advantages of CMAC identification, adaptive learning and control techniques. In these systems, the on-line parameter training methodologies, using the gradient descent method and the Lyapunov stability theorem, are proposed to increase the learning capability. Moreover, the applications of these systems in servomotor control, biped robot and computer-aided diagnostic of breast nodules are demonstrated.

Brief Biography of the Speaker:
Prof. Chih-Min Lin is currently a Professor of Electrical Engineering, Yuan Ze University, Taiwan. He also serves as the Editor-in-Chief of WSEAS Trans. Systems and Control and Associate Editor of IEEE Trans. Systems, Man, and Cybernetics, Part B; Asian Journal of Control; and International Journal of Fuzzy Systems.. He is now the Chair of IEEE Computational Intelligence Society, Taipei Chapter, and Board of Government of IEEE Taipei Section. His research interests include fuzzy systems, neural network, cerebellar model articulation controller, and intelligent control systems. He is an IEEE Fellow and IET Fellow. He has published 93 journal papers and 134 conference papers.