Iterative Improvement of trigonometric networks

The trigonometric network, introduced in this paper, is a multilayer feedforward neural network with sinusoidal activation functions. Unlike the N-dimensional Fourier series, the basis functions of the proposed trigonometric network have no strict harmonic relationship. An effective training algorithm for the network is developed. It is shown that the trigonometric network performs better than the sigmoidal neural network for some data sets. A pruning method based on the modified Gram-Schmidt orthogonalization procedure is presented to detect and prune useless hidden units. Other network architectures related to the trigonometric network, such as the sine network, are shown to be inferior to the network proposed in this paper.