Masters Theses
Novel optimization methods for scalar and vector quantization design
Abstract
"Computational Intelligence (CI) aims to provide solutions to real-life problems using mathematical models which mimic artificial intelligence. Many CI paradigms, like Neural Networks and Evolutionary Computation, offer advantages in a variety of applications in digital signal processing. In this thesis, several CI techniques, namely Neural Networks, Particle Swarm Optimization (PSO), Differential Evolution (DE) and Adaptive Critic Designs (ACDs), are applied to scalar and vector quantization problems"--Abstract, page iii.
Advisor(s)
Venayagamoorthy, Ganesh K.
Committee Member(s)
Smith, Scott C.
Grant, Steven L.
Department(s)
Electrical and Computer Engineering
Degree Name
M.S. in Computer Engineering
Sponsor(s)
National Science Foundation (U.S.)
Publisher
University of Missouri--Rolla
Publication Date
Spring 2006
Pagination
xii, 204 pages
Note about bibliography
Includes bibliographical references.
Rights
© 2006 Wenwei Zha, All rights reserved.
Document Type
Thesis - Citation
File Type
text
Language
English
Subject Headings
Mathematical optimizationProgram transformation (Computer programming)Scalar field theorySwarm intelligenceVector processing (Computer science)
Thesis Number
T 8974
Print OCLC #
85548002
Recommended Citation
Zha, Wenwei, "Novel optimization methods for scalar and vector quantization design" (2006). Masters Theses. 3882.
https://scholarsmine.mst.edu/masters_theses/3882
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