Adaptive Filters Adaptive structures The least mean square (LMS) algorithm Programming examples using C and TMS320C3x code Adaptive filters are best used in cases where signal conditions or system parameters are slowly changing and the filter is to be adjusted to compensate for this change. The least mean square (LMS) criterion is a search algorithm that can be used to provide the strategy for adjusting the filter coefficients. Programming examples are included to give a basic intuitive understanding of adaptive filters. . | Digital Signal Processing Laboratory Experiments Using C and the TMS320C31 DSK Rulph Chassaing Copyright 1999 John Wiley Sons Inc. Print ISBN 0-471-29362-8 Electronic ISBN 0-471-20065-4 7 Adaptive Filters Adaptive structures The least mean square LMS algorithm Programming examples using C and TMS320C3x code Adaptive filters are best used in cases where signal conditions or system parameters are slowly changing and the filter is to be adjusted to compensate for this change. The least mean square LMS criterion is a search algorithm that can be used to provide the strategy for adjusting the filter coefficients. Programming examples are included to give a basic intuitive understanding of adaptive filters. INTRODUCTION In conventional FIR and IIR digital filters it is assumed that the process parameters to determine the filter characteristics are known. They may vary with time but the nature of the variation is assumed to be known. In many practical problems there may be a large uncertainty in some parameters because of inadequate prior test data about the process. Some parameters might be expected to change with time but the exact nature of the change is not predictable. In such cases it is highly desirable to design the filter to be self-learning so that it can adapt itself to the situation at hand. The coefficients of an adaptive filter are adjusted to compensate for changes in input signal output signal or system parameters. Instead of being rigid an adaptive system can learn the signal characteristics and track slow changes. An adaptive filter can be very useful when there is uncertainty about the characteristics of a signal or when these characteristics change. Figure shows a basic adaptive filter structure in which the adaptive filter s output y is compared with a desired signal d to yield an error signal e which is fed back to the adaptive filter. The coefficients of the adaptive filter are 195 196 Adaptive Filters FIGURE Basic adaptive filter .
