Generalization of Neural Network Model (CMAC) for Coordinate Transformation in Neural Computation
神经计算中坐标变换的网络模型(CMAC)的泛化特性

Generalization of neural network is a very important topic for coordinate transformation in neural computation. In this paper, we describe the principle of Cerebellar Model Articulation Controller (CMAC) including its learning algorithm, and discusse the generalization of CMAC through simulation of coordinate transformation (the input is position coordinate values and the output is articulation degrees of robot). The CMAC may still run well at generalization rate 1:100. Several factors affecting the accuracy are also discussed.