Abstract
To describe the performance of a new kind of approximation algorithm using B-Spline weight functions on feedforward neural networks, the analysis of generalization is proposed in this paper. The neural networks architecture is very simple and the number of weight functions is independent of the number of patterns. Three important theorems are proved, which mean that, by increasing the density of the knots, the upper bound of the networks error can be made to approach zero. The results show that the new algorithm has good property of generalization and high learning speed.
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