Applications of particle swarm optimization for neural network training and digital systems
Keywords and Phrases
Particle Swarm Optimization (PSO)
"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.
Electrical and Computer Engineering
M.S. in Computer Engineering
University of Missouri--Rolla
xi, 83 leaves
© 2004 Venu Gopal Gudise, All rights reserved.
Thesis - Citation
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=b5129597~S5
Gudise, Venu Gopal, "Applications of particle swarm optimization for neural network training and digital systems" (2004). Masters Theses. 2515.
Share My Thesis If you are the author of this work and would like to grant permission to make it openly accessible to all, please click the button above.