CONSTRUCTIVE THEORY FOR B\|SPLINE NEURAL NETWORK

The characteristics of B\|spline basis function is first discussed, based on which B\|spline neural network is proved to be a universal approximator. And then a constructive algorithm is presented. It is proved that this algorithm can be used to build a B\|spline neural network with minimum hidden units to approximate any continuous function defined on compact set to a prescribed accuracy.