一种新型径向基函数神经网络的非线性系统逼近

讨论了一种新的、正弦型径向基函数(SRBF)神经网络，并用来逼近n堆连续函数。该SRBF所采用的n堆正弦型的基函数是光滑的,并且是致密的。该SRBF网络的权因子是输入的低阶多项式函数。本文给出的一种简单计算程序，显著地降低了网络训练和计算时间。并且由于SRBF的基函数可以非均匀的量化格点为中心。因而降低了网络所需存储的样本数，网络的输出及其一阶导数都是连续的。对于非线性系统。该SRBF网络在系统定义城内的逼近是精确的。并且在存储参数的个数上是最优的。通过实例仿真，证明该方法步骤简单,训练速度快，精度也很理想。

Approximation of Nonlinear Systems with a New Radial Basis Function Neural Networks

A new Sine radial basis function(SRBF) neural network which is used to approximate a continuous function ofn vari- ables is presented.The SRBF uses an n dimensional raised sine type ofthat RBF is smooth,yethas compactsupport.The SRBF network coefficients are low order polynomial functions of the input.A simple computational procedure is presented which signifi- cantly reduces the network training and evaluation time.Storage space is also reduced by allowing for a nonuniform grid of points about which the SRBFs are centered.The network outputis shown to be continuous and have a continuous firstderivative.Forthe nonlinear system,the SRBF network repersentation is exacton the domain overwhich itis defined,and itisoptimal in terms of the number of distinctstorage parameters required.Several examples are presented which illustrate the algorithm is concise,effective and accurate.