Adaptive Noise Cancellation (ANC) entails estimation of signals corrupted by additive noise or other interference. ANC utilizes a “reference” signal correlated in some way with the “primary noise” in the noise cancellation process. In ANC, the reference signal is adaptively filtered and thereafter subtracted from the “primary” input to obtain the desired signal estimate. Adaptive filtering before the subtraction process allows for handling of inputs that are either deterministic or stochastic, stationary or time varying. ANC has been widely applied in the fields of telecommunication, radar and sonar signal processing. The performance and efficiency of ANC schemes is based on how well the filtering algorithm can adapt to the changing signal and noise conditions. It is worthwhile focusing on developing better variants of AI algorithms from the point of view of ANC. This thesis is focused on: development of a modified version of the Simulated Annealing (SA) algorithm and its application in ANC. This is alongside an analysis of the effectiveness of the standard and modified SA algorithms in ANC in comparison to standard Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) algorithms. Signals utilized in this study include: sinusoidal signals, fetal electrocardiogram signals and randomly generated signals. The modified SA algorithm has been developed on the basis of making modifications to the control parameters of the standard SA on the basis of the acceptance probability and the cooling schedule. A low complexity acceptance probability scheme has been proposed. The proposed cooling schedule is iteration-adaptive to improve on algorithm convergence. The ANC problem is formulated as a minimization problem entailing the minimization of the difference between a noise contaminated signal and a weighted estimate of the noise content. This is achieved through optimal ANC tap-weight adjustment. The algorithms under study are applied in the weight generation process with the expected outcome as ideally a noise free signal. In this evaluation, performance measures analyzed in the study are mis-adjustment and convergence rate. To evaluate these, Euclidean distances and the correlation factors between the desired signal

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