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Convergence Investigation of LMS Algorithms with Time-Varying Step Size

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dc.contributor.author Khomentrakarn, Chusak
dc.date.accessioned 2019-03-19T18:34:34Z
dc.date.available 2019-03-19T18:34:34Z
dc.date.issued 1990-05
dc.identifier.citation C. Khomentrakarn, "Convergence Investigation of LMS Algorithms with Time-Varying Step Size", Masters thesis, Dept. of Engineering, Univ. of Bridgeport, Bridgeport, CT, 1990. en_US
dc.identifier.uri https://scholarworks.bridgeport.edu/xmlui/handle/123456789/4034
dc.description This thesis is being archived as a Digitized Shelf Copy for campus access to current students and staff only. We currently cannot provide this open access without the author's permission. If you are the author of this work and desire to provide it open access or wish access removed please contact the Wahlstrom Library to discuss permission. en_US
dc.description.abstract An adaptive linear combiner has been used in many applications. One common adaptive algorithm for this field is the Least Mean Squared (LMS) algorithm which can be applied to both a stationary and a non-stationary input. This algorithm uses a fixed step size to train a process. A small step size provides the accuracy of an optimum value with less variance but slow convergence. Large step size gives fast convergence but is constrained by convergent conditions. There are a variety of strategies to vary step size according to these advantages and disadvantages. Three strategies are presented here: the Normalized Least Mean Squared algorithm, the Error Dependent Step-Size algorithm and the Independent Step-Size algorithm. The speed of convergence is compared by using the time constant of the mean squared error curve (the learning curve). Only stationary inputs are considered here. Simulation with both deterministic input and stochastic input is included. en_US
dc.language.iso en_US en_US
dc.subject Least mean squared en_US
dc.subject Algorithms en_US
dc.title Convergence Investigation of LMS Algorithms with Time-Varying Step Size en_US
dc.type Thesis en_US
dc.institute.department School of Engineering en_US
dc.institute.name University of Bridgeport en_US


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