Application of swarm and evolutionary techniques to neural network training and partitioning combinational CMOS

Author

Gaurav Singhal

Keywords and Phrases

Particle Swarm Optimization (PSO); Primary Input and FANout (PIFAN)

Abstract

"Swarm Intelligence...involves a population of simple agents interacting locally with one another and with their environment, leading to the emergence of global behavior. Particle Swarm Optimization (PSO) and quantum-inspired evolutionary algorithm (QEA) are two such evolutionary computation techniques. Much simulation work exists in the literature on evolutionary algorithms performing well on optimization problems, but few hardware applications are reported. This thesis aims at finding suitable algorithms for hardware implementation on digital processor. Thus, Binary PSO, QEA and Improved QEA are investigated on basic nonlinear function approximation problems"--Abstract, page iii.

Advisor(s)

Venayagamoorthy, Ganesh K.

Committee Member(s)

Smith, Scott C.
Wu, Cheng Hsiao

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

xiii, 226 pages

Rights

© 2006 Gaurav Singhal, All rights reserved.

Document Type

Thesis - Citation

File Type

text

Language

English

Subject Headings

Genetic algorithms
Metal oxide semiconductors, Complementary -- Design and construction
Neural networks (Computer science)
Swarm intelligence

Thesis Number

T 8972

Print OCLC #

85485442

Link to Catalog Record

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

Recommended Citation

Singhal, Gaurav, "Application of swarm and evolutionary techniques to neural network training and partitioning combinational CMOS" (2006). Masters Theses. 3880.
https://scholarsmine.mst.edu/masters_theses/3880