Applications of particle swarm optimization for neural network training and digital systems

Author

Venu Gopal Gudise

Keywords and Phrases

Particle Swarm Optimization (PSO)

Abstract

"Particle Swarm Optimization (PSO) is an evolutionary computation technique similar to genetic algorithm, which is a population (swarm) based optimization tool. PSO starts with a population of random solutions called particles. Each particle is given a random velocity and is flown through the problem space. The particles work together to achieve a global task. The best particle of the entire swarm is taken as the final solution to the task. In this thesis, three problems are studied using the PSO; their results are presented, compared and contrasted with results obtained using conventional techniques."--Abstract, page iii.

Department(s)

Electrical and Computer Engineering

Degree Name

M.S. in Computer Engineering

Publisher

University of Missouri--Rolla

Publication Date

Spring 2004

Pagination

xi, 83 pages

Note about bibliography

Includes bibliographical references.

Rights

© 2004 Venu Gopal Gudise, All rights reserved.

Document Type

Thesis - Citation

File Type

text

Language

English

Subject Headings

Swarm intelligence
Genetic algorithms

Thesis Number

T 8503

Print OCLC #

56576661

Link to Catalog Record

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

Recommended Citation

Gudise, Venu Gopal, "Applications of particle swarm optimization for neural network training and digital systems" (2004). Masters Theses. 2515.
https://scholarsmine.mst.edu/masters_theses/2515