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Miller, and D. Dietz, Development and application of CMAC neural network-based control, in Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches, New York: Van Nostrand-Reinhold, 1992, pp. 215–232. 20. L. G. Kraft and D. P. Campagna, A comparison between CMAC neural network control and two traditional adaptive control systems, IEEE Control Syst. , 10: 36–43, 1990. 21. L. G. Kraft and D. P. Campagna, A summary comparison of CMAC neural network and traditional adaptive control systems, CHANNEL CAPACITY in Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches, New York: Van Nostrand-Reinhold, 1992, pp.

W Figure 1. CMAC architecture for the approximation of a vector function. CEREBELLAR MODEL ARITHMETIC COMPUTERS 155 1 n=1 0 λ 1 λ 2 λ 3 λ 4 λ5 λ 6 λ 7 λ 8 λ 9 x λ 1 λ 2 λ 3 λ 4 λ5 λ 6 λ 7 λ 8 λ 9 x λ 1 λ 2 λ 3 λ 4 λ5 λ 6 λ 7 λ 8 λ 9 x 1 n=2 0 1 n=4 0 (a) (b) Figure 2. Standard CMAC receptive field functions of orders 1, 2, and 4. similar to two input patterns x1 and x2 will be determined by extent of overlap of Ͱ*1 and Ͱ*2 (22). Similarly the network’s ability to dichotomize or produce dissimilar outputs for the two inputs patterns x1 and x2 depends on the nonintersecting elements of Ͱ*1 ∧ Ͱ*2 .

45. S. Commuri and F. L. Lewis, Discrete-time CMAC neural networks for control applications, Proc. IEEE Conf. Decision Control, 1995. 46. K. S. Narendra and A. M. Annaswamy, A new adaptive law for robust adaptation without persistent adaptation, IEEE Trans. Autom. Control, 32: 134–145, 1987. 47. F. L. Lewis, C. T. Abdallah, and D. M. Dawson, Control of Robot Manipulators, New York: Macmillan, 1993. 25. D. Campagna and L. G. Kraft, Stability analysis of open-loop learning in CMAC neural networks, preprint, 1998.

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39.Neural Networks by John G. Webster (Editor)

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