Wavelet neural networks: a design perspective

The application of wavelets in the fields of mathematics and engineering has grown rapidly in the past few years. One interesting application is to use wavelets as the activation functions in neural networks. This paper discusses the theoretical background involving wavelets from which feedforward wavelet neural networks are simply a direct consequence and evaluates a design procedure for developing these multiresolution networks. Two different wavelet neural network design examples are presented in order to demonstrate the issues involved in the design of wavelet networks. One example is to use a wavelet neural network to solve a two dimensional nonlinear function approximation problem. The other example is to use a wavelet neural network for feathering the position of the solar arrays of the Space Station.<<ETX>>

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