摘要
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 .
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.
出处
《软件学报》
EI
CSCD
北大核心
2000年第1期133-137,共5页
Journal of Software
基金
江苏省科委应用基础基金! (No.BJ9712 2 )资助
关键词
小脑模型
神经网络
仿真
精度
算法
Cerebella model articulation controller (CMAC), neural network, associative interpolation, simulation, accuracy, algorithm.
