Application of swarm and evolutionary techniques to neural network training and partitioning combinational CMOS
Keywords and Phrases
Particle Swarm Optimization (PSO); Primary Input and FANout (PIFAN)
"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, leaf iii.
Venayagamoorthy, Ganesh K.
Smith, Scott C.
Wu, Cheng Hsiao
Electrical and Computer Engineering
M.S. in Computer Engineering
National Science Foundation (U.S.)
University of Missouri--Rolla
xiii, 226 leaves
© 2006 Gaurav Singhal, All rights reserved.
Thesis - Citation
Library of Congress Subject Headings
Metal oxide semiconductors, Complementary -- Design and construction
Neural networks (Computer science)
Print OCLC #
Link to Catalog Record
Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.http://laurel.lso.missouri.edu/record=b5790880~S5
Singhal, Gaurav, "Application of swarm and evolutionary techniques to neural network training and partitioning combinational CMOS" (2006). Masters Theses. 3880.