联想记忆系统学习算法的改进  

Improvement of Learning Algorithm for the Associative Memory System

作　　者：邢春峰[1] 王信峰[1] 

机构地区：[1]北京联合大学基础部,北京100101

出　　处：《数学的实践与认识》2004年第9期92-96,共5页Mathematics in Practice and Theory

摘　　要：借助于牛顿向后插公式对文 [1 ]的 NFI-AMS学习算法进行了改进 ,改进后的联想记忆系统的学习算法不但具有原来学习算法的收敛速度快、学习精度高等优点 ,而且还具备了 CMAC-AMS本身具有的局部泛化 (generalaization亦称推广 )能力以及对周围信息的收集能力大大增强等等 .数值模拟表明 ,这种改进的NFI-AMS在信号处理。The Associative Memory System based on Newton′s Forward Interpolation formula(NFI-AMS) is improved by Newton's backward Interpolation formula. Improved learning algorithm posses not only the advantages over NFI-AMS in fast convergence rate and high precision of learning, but also the advantages over part generalaization of CMAC-AMS and strengthens collection information. Numerical simulations have shown that application areas of signal processing, pattern recognition, and controller implementation for high-precision real-time intelligent control.

关 键 词：学习算法 联想记忆 CMAC 实时 模式识别 智能控制 泛化 系统学习 能力 公式 

分 类 号：O241[理学—计算数学]