Hierarchical wavelet neural networks
Sathyanarayan S. Rao, Ravikanth Pappu · 2002
Neural networks can be used in nonlinear system modeling and prediction applications. Wavelet decomposition provides a method of examining a signal at multiple scales. The authors draw upon the connection between these two fields. A method is outlined which exploits the localized, hierarchical nature of wavelets in the learning of time series. This is achieved by having a dynamic network-one in which nodes are added to the network so as to progressively reduce the modelling error. This cascade correlation approach overcomes some of the disadvantages of a static network architecture. The learning algorithm is outlined, and its performance is demonstrated using simulations.>