Introduction to Grey system theory
The Journal of Grey System
Single-input CMAC control system
Neurocomputing
Expert Systems with Applications: An International Journal
Review: Application of CMAC neural network to the control of induction motor drives
Applied Soft Computing
Self-organizing CMAC control for a class of MIMO uncertain nonlinear systems
IEEE Transactions on Neural Networks
Smooth trajectory tracking of three-link robot: a self-organizingCMAC approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Credit assigned CMAC and its application to online learning robust controllers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Kernel CMAC With Improved Capability
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
High-order MS CMAC neural network
IEEE Transactions on Neural Networks
A self-organizing HCMAC neural-network classifier
IEEE Transactions on Neural Networks
Grey-Based particle swarm optimization algorithm
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
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This study attempts to develop a grey adaptive growing cerebellar model articulation controller (CMAC) network, which is constructed by connecting several 1D Albus' CMACs as a two-level tree structure. Even though the target function is unknown in advance, grey relational analysis still can analyze the learning performance between the network outputs and the target values. According to the result of grey relational analysis, the proposed adaptive growing mechanism could determine whether a specific region covered by a state or a CMAC needs to be repartitioned or not. By this way, not only the number of 1D CMACs but also the number of states could be gradually increased during the learning process. And then the purpose of self-organizing input space can be attained. In addition, the linear interpolation scheme is applied to calculate the network output and for simultaneously improving the learning performance and the generalization ability. Simulation results show that the proposed network not only has the adaptive quantization ability, but also can achieve a better learning accuracy and a good generalization ability with less memory requirement.