Initialization by selection for wavelet network training. (English) Zbl 1009.68843

Oussar, Yacine; Dreyfus, Gérard

Neurocomputing 34, No. 1-4, 131-143 (2000).

Summary: We present an original initialization procedure for the parameters of feedforward wavelet networks, prior to training by gradient-based techniques. It takes advantage of wavelet frames stemming from the discrete wavelet transform, and uses a selection method to determine a set of best wavelets whose centers and dilation parameters are used as initial values for subsequent training. Results obtained for the modeling of two simulated processes are compared to those obtained with a heuristic initialization procedure, and the effectiveness of the proposed method is demonstrated.

MSC:

68U99	Computing methodologies and applications
68T05	Learning and adaptive systems in artificial intelligence

Keywords:

Wavelet networks; Training; Initializing parameters; Nonlinear static modeling