CMAC算法中泛化特性分析

The Analysis of CMAC Generalization

CMAC 算法中,研究泛化性能是其中一项主要内容.泛化性能好,则网络的学习精度高.本文阐述网络的原理、结构、学习算法,对影响泛化性能的量化精度、采样精度及其之间的关系进行理论分析.并通过计算机仿真验证了当量化精度等于采样精度、量化精度大于采样精度时对网络精度的影响,得出量化精度应该大于采样精度的结论.提出一种利用基于多目标的遗传算法来确定泛化常数和量化精度的方法,并通过实例验证方法的正确性.

Generalization is very important in Cerebellar Model Articulation Controller （CMAC）. If CMAC has good generalization, it will have high precision. In this paper, the principle, structure and learning algorithm of CMAC are described. The relationship between the quantification precision and sampling precision that influences the generalization is discussed theoretically . Simulation results show the correctness of relationship between the quantification and sampling precision, and the conclusion that the quantification precision should be higher than the sampling precision is gotten. Moreover, a new kind of optimization based on pareto genetic algorithm （PGA） about generalization parameter and quantification precision is proposed. Experimental results show the correctness of the new method.