基于牛顿前向插值公式的联想记忆系统

A Novel Associative Memory System Based on Newton's Forward Interpolation

基于牛顿前向插值公式提出一种对任意阶多维函数可实现无差逼近的新型CMAC联想记忆系统,详细讨论了该系统的原理、插值算法及训练规则,仿真实例表明了系统的可行性与有效性.该系统在信号处理、模式识别及高精度实时智能控制等领域具有广泛的应用价值.

A novel highorder associative memory system based on the Newton's forward Interpolation (NFIAMS) is preposed, which can carry out errorfree approximations to multivariable polynomial functions of arbitrary order. The theory, interpolation algorithm and traning rules of the NFIAMS are discussed in detail. The results of simulating examples indicate that it has more advantadges than CMACtype AMS, such as highprecision of learning, much smaller memory requirement without the datacollision problem and much less computational effort for training and faster convergence rates than that attainable with multilayer BP neural networks.