Publications Details
Adaptive inverse filter
This paper describes the design of an inverse adaptive filter, using the Least-Mean-Square (LMS) algorithm, the correct data taken with an analog filter. The gradient estimate used in the LMS algorithm is based upon the instantaneous error, e{sup 2}(n). Minimizing the mean-squared-error does not provide an optimal solution in this specific case. Therefore, another performance criterion, error power, was developed to calculate the optimal inverse model. Despite using a different performance criterion, the inverse filter converges rapidly and gives a small mean-squared-error. Computer simulations of this filter are also shown in this paper.