Novel optimization methods for scalar and vector quantization design

Author

Wenwei Zha

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 optimization
Program transformation (Computer programming)
Scalar field theory
Swarm intelligence
Vector processing (Computer science)

Thesis Number

T 8974

Print OCLC #

85548002

Link to Catalog Record

Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.

Recommended Citation

Zha, Wenwei, "Novel optimization methods for scalar and vector quantization design" (2006). Masters Theses. 3882.
https://scholarsmine.mst.edu/masters_theses/3882