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Digital communication systems simulation (QAM)
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MATLAB simulation program integrated QAM digital communication system available for everybody to study the reference. Includes transmitter, coding, modulation, channel, demodulation, decoding, receiver code, a number of pictures for reference.
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Simulation of LMS algorithm in adaptive signal pro
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LMS algorithm for adaptive signal processing in the course of the simulation codes, were written by himself, for reference only, fig. Full name of Least mean square algorithm. Chinese is the algorithm. Sensors and adaptive linear element in history almost simultaneously raised, and the resulting in adjustments to weighting algorithms are very similar. They are learning algorithm based on error-correcting learning rule. Perceptron algorithm has the following problems: cannot be extended to General network; function is not linearly separable, without any outcome. United States Stanford University Widrow and Hoff in the study made by the theory of Adaptive LMS algorithm, due to its easy and quickly got used, become the standard algorithms of adaptive filtering.
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LMS and RLS algorithm and its application in balan
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Adaptive signal processing in the classic balance of LMS and RLS algorithm applied to communication, and image for verification. LMS Least mean square algorithm for the opener. Chinese is the algorithm. Sensors and adaptive linear element in history almost simultaneously raised, and the resulting in adjustments to weighting algorithms are very similar. They are learning algorithm based on error-correcting learning rule. Perceptron algorithm has the following problems: cannot be extended to General network; function is not linearly separable, without any outcome. United States Stanford University Widrow and Hoff in the study made by the theory of Adaptive LMS algorithm, due to its easy and quickly got used, become the standard algorithms of adaptive filtering. Recursive least square (RLS) the basic idea of the algorithm is to make every moment for all input signals, revaluation, minimize the weighted sum of squared errors, which m
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