The Complete SPRLIB & ANNLIB

adapt_wavnet: - adapt a wavelet network after presentation of one learning sample

int adapt_wavnet

(proj, net, sample, alpha, momentum, L, U, comp, domain)

`int`	`proj`	Specify if wavelets outside the domain of the function to be approximated should be truncated or not
`NET`	*`net`**	Wavelet network
`SAMPLE`	*`sample`**	Current sample to be evaluated
`double`	`alpha`	Learning rate in gradient descent wavelet network training
`double`	`momentum`	Momentum term value
`double`	`L`	Lower value for scaling the dataset prior to training
`double`	`U`	Upper value for scaling the dataset prior to training
`int`	`comp`	Parameter specifying the maximal amount of compression of a wavelon
`int`	`domain`	Parameter specifying the tolerance for truncation of wavelons outside the domain of the function to be approximated

TRUE if an error occurred, FALSE otherwise.

This function performs one step in the gradient descent procedure: new parameter values (dilations, translations, weights) are computed and adapted.

`adapt_wavnet`