The interpolation capabilities of the binary CMAC
Neural Networks
Improving the Generalization Capability of the Binary CMAC
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Kernel CMAC With Improved Capability
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hardware implementation of CMAC neural network with reduced storage requirement
IEEE Transactions on Neural Networks
A self-organizing HCMAC neural-network classifier
IEEE Transactions on Neural Networks
Neighborhood sequential and random training techniques for CMAC
IEEE Transactions on Neural Networks
Using CMAC for mobile robot motion control
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
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Cerebellar Model Articulation Controller (CMAC) has some attractive features: fast learning capability and the possibility of efficient digital hardware implementation. Besides these attractive features it has a serious drawback: its memory complexity may be very large. In multidimensional case this may be so large that practically it cannot be implemented. To reduce memory complexity several different approaches were suggested so far. Although these approaches may greatly reduce memory complexity we have to pay a price for this complexity reduction. Either both modelling and generalization capabilities are deteriorated, or the training process will be much more complicated. This paper proposes a new approach of complexity reduction, where properly constructed hash-coding is combined with regularized kernel representation. The proposed version exploits the benefits of kernel representation and the complexity reduction effect of hash-coding, while smoothing regularization helps to reduce the performance degradation.