The application of wavelet neural networks to adaptive transform coding of one dimensional signals

The inverse receptive field partition (IRFP) algorithm developed from the receptive field partition (RFP) algorithm, operates on a wavelet basis function network. This technique is a fast method of basis function selection. It begins with the highest resolution wavelet basis functions as its seed functions. Like RFP, IRFP then uses receptive field activation principle during training. This principle insures only wavelets within a specified interval are chosen as candidates. By way of the receptive activation principle, wavelets are selected with the best fit from a precalculated development pool and moved to the main pool. The selection of wavelet coefficients for the main pool is based on best fit across all resolutions. Overall functional fit can be controlled by global MSE and pruning thresholds.