A new CMAC neural network model with adaptive quantization input layer
Gao Xiaozhi, Wang Chang-hong, Xiaori Gao, S.J. Ovaska · 2002
We first discuss the structure, principle and learning algorithm of the cerebellar model arithmetic controller (CMAC) neural network model. A new adaptive quantization method based on competitive learning is then proposed to quantize the inputs of the CMAC according to the degree of variations of the approximated function. Theoretical analysis and simulation results show that with the input layer using this algorithm the CMAC can provide a more accurate and efficient approximation than the original model using equal-size quantization method.