Abstract
The cerebellar model articulation controller (CMAC) is often used in learning control. It can be viewed as a basis function network (BFN). The conventional CMAC uses local constant basis functions. A disadvantage is that its output is constant within each quantized state and the derivative information is not preserved. If the constant basis functions are replaced by non-constant differentiable basis functions, the derivative information will be able to be stored into the structure as well. In this paper, the generalized scheme that uses general basis functions is investigated. The conventional CMAC is a special case of the generalized technique. The mathematical foundation for the modified scheme is derived and the convergence of learning is proved. Simulations for the CMAC with Gaussian basis functions (GBFs) are performed to demonstrate the improvement of accuracy in modeling, and the capability in providing derivative information. Copyright © 1996 Elsevier Science Ltd
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