CMAC Study with Adaptive Quantization

An adaptive quantified strategy of cerebellar model arithmetic controller (CMAC) is proposed in this paper. Traditionally, the CMAC using equal-size sampling point has attractive properties of learning convergence and speed. However, in many practical applications, fix memory sizes are used. Hence, it is an important subject to make the choice between accuracy and memory sizes of the CMAC. Therefore, quantization plays an important role in construction of CMAC. In this paper, the quantization problem of CMAC is study. By using equal-sized quantization, CMAC cannot use finite knots to represent the variation of the reference signal efficiently. Therefore, an adaptive quantization rule is proposed to solve these problems.