DEVELOPMENT AND APPLICATIONS OF ADAPTIVE IIR AND SUBBAND FILTERS Master of

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3, the adaptive processor attempts to recover a delayed version of the signal ‘s’, which is assumed to have been altered by the slowly varying channel with additive noise. In the adaptive filtering task, adaptation refers to the method by which the parameters of the system are changed from time index n to time index (n + 1). To overcome the limit of a DE, the optimal configuration in which an indirect equalizer is combined with the DE is proposed. The LMS algorithms for adapting the DE and the ECI are given by b(k + 1) = b(k ) + μ b e b (k ) y (k ) (4. These signal samples can be manipulated by either at higher or lower clock rates by a process called interpolation or decimation. Examples of some important applications include system identification, channel equalization, linear prediction, echo cancellation, and adaptive array processing. 11 Convergence performance of the SAF for M=2 with system noise absent (filter length L=80), No of training samples = 4,000 . It was also given that the performance comparison between the LMS and RLS based adaptive IIR channel equalization and observed that the RLS based IIR equalizer gives superior performance than the LMS convergence. The approach with individual filters in the subband, as depicted in Fig. Different configurations of subband adaptive filters exist. Internal plant noise appears at the plant output and is commonly represented there as an adaptive noise. 67 Subband Adaptive Filter h0 D x0(n) w0(n) x(n) y0(n) D g0 y(n) Weight update hM-1 x (n) D M-1 wM-1(n) d(n) - yM-1(n) D gM-1 D h0 Weight update e0(n) e(n) eM-1(n) D hM-1 Fig. 5 this process is achieved by an upsampler and a low pass filter g (n). If mutation probability is 100%, whole chromosome is changed, if it is 0%, nothing is changed. 14) Similarly, Y0 ( z ) = 1 G0 ( z ) [H 0 ( z ) X ( z ) + H 0 (− z ) X (− z ) 2 ] (5. 2 Motivation Adaptive filtering has a tremendous application in the field of signal processing and communications such as system identification, channel equalization, linear prediction, and noise cancellation etc. the adaptive algorithm that describes how the parameters are adjusted from one time instant to the next. On the other hand, if there are too many chromosomes, GA slows down. This technique yield improved convergence rate when the number of bands in the filter is increased. This thesis also examines enhanced structured stochastic global optimization algorithms for adaptive IIR filtering, with focus on an algorithm named Genetic Algorithm (GA)