Applications of Radius Basis Function Neural Networks in Numerical Calculation

Many numeric arithmetics derived from classical function approach theories have some flaws, such as too much complicated computation and so on. So, they are restricted in the real application. Neural networks can be used to compute the relationship between complicated inputs and outputs, so neural networks have a strong ability to approximate functions. Structure and study process of radius basis function neural networks ( RBFNN) .applications of RBFNN in functional approximating, solving nonlinear multivariable equation systems and scattered data interpolation are put forward in the paper. Simultaneously, numerical examples are given combining with the toolbox of MATLAB neural networks and experimental results are compared with the BP neural networks. It is made clear that RBFNN is a powerful tool for numeric computation. It is of great value when RBFNN is made into software package to resolve numerical problems.