提高小脑模型神经网络精度的算法及仿真应用

CMAC(cerebella model articulation controller)神经网络的局部结构使得学习非线性函数更快.然而,在许多应用领域,CMAC的学习精度不能满足应用要求.该文提出了一种改进CMAC学习精度的联想插补算法,同时给出了一个仿真实验.其结果表明,使用此算法,改进的CMAC的学习精度比改进前提高了10倍,学习收敛也更快.

An Algorithm for Improving the Accuracy of Cerebella Model Articulation Controller Neural Networks and Simulation Application

The local structure of CMAC (cerebella model articulation controller) neural networks results in faster learning of nonlinear functions. However, the learning accuracy of CMAC is too low to meet the requirements of application in many fields. Hence, an associative interpolation algorithm is proposed in this paper for improving the learning accuracy of CMAC. Meanwhile, a simulation experiment is described. Its result shows that the learning accuracy of the improved CMAC is ten times higher than that of the original CMAC, and the learning convergence is also faster.