Letters: Data classification using hybrid GrayART network
Neurocomputing
Invariant 2D object recognition using KRA and GRA
Expert Systems with Applications: An International Journal
Standalone CMAC control system with online learning ability
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
CMAC neural networks structures
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
ART-type CMAC network classifier
Neurocomputing
Indirect adaptive self-organizing RBF neural controller design with a dynamical training approach
Expert Systems with Applications: An International Journal
Adaptive dynamic CMAC neural control of nonlinear chaotic systems with L2 tracking performance
Engineering Applications of Artificial Intelligence
Particle swarm optimization with grey evolutionary analysis
Applied Soft Computing
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This paper attempts to incorporate the structure of the cerebellar-model-articulation-controller (CMAC) network into the Kohonen layer of the self-organizing map (SOM) to construct a self-organizing CMAC (SOCMAC) network. The proposed SOCMAC network can perform the function of an SOM and can distribute the learning error into the memory contents of all addressed hypercubes as a CMAC. The learning of the SOCMAC is in an unsupervised manner. The neighborhood region of the SOCMAC is implicit in the structure of a two-dimensional CMAC network and needs not be defined in advance. Based on gray relational analysis, a credit-assignment technique for SOCMAC learning is introduced to hasten the overall learning process. This paper also analyzes the convergence properties of the SOCMAC. It is shown that under the proposed updating rule, both the memory contents and the state outputs of the SOCMAC converge almost surely. The SOCMAC is applied to solve both data-clustering and data-classification problems, and simulation results show that the proposed network achieves better performance than other known SOMs.