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.