This work deals with the use of previous or colateral information to improve the behaviour of adaptive algorithms. The study is made on gradient-baseq methods due te the relatively simple and good performances that they use to exhibit. This paper shows that the complete knowledge of the data at the input of the adaptive filter (and in consequence of its autocorrelation matrix and its inverse) can be used to modify the classic L.M.S. algorithm leading to new expressions for the gradient and for the optimum 1step size 1 , alternative, in sorne cases, to the Powell expression. Finally, the description is completed with the comparison between the variation ranges and VLSI implementation cost for this two optimum 'step size' values anda natural generalization set of parameter is obtained.

Peer Reviewed