Doctoral Dissertations

Abstract

"Artificial neural networks (ANNs) have been developed as adaptable, robust function approximators for at least the last quarter-century. They have progressed through two generations, and the third is now under development. Spiking neural networks (SNNs) seek to improve on previous generations in two ways: by using a more biologically-inspired neuron, they are shown to be capable of more complex calculations; incorporating polychronous properties of highly-recurrent networks with delays of different lengths on each synapse to achieve large numbers of possible patterns with relatively few neurons and synapses.

Abstracted spiking neurons have been used as a third-generation activation function in a traditional feedforward network architecture, and their potency in application to a real-world problem -- identification of power system generator dynamics -- is demonstrated in this dissertation in comparison to a standard sigmoidal multi-layer perceptron network. However, the goal of SNNs is to be able to utilize biological-like neural network modeling to capture the computational prowess of living brains. In order to achieve such a feat, first a bio-inspired SNN must be able to handle continuous-valued function approximation; until this is done, such networks cannot even be compared to their second-generation predecessors.

This dissertation demonstrates a technique for using a faithfully modeled SNN on continuous-valued problems. The encoding and decoding frameworks developed in this dissertation for the biologically-inspired SNN enables it, like any other ANN, to be applied to any time-dependent problem, including neuroidentification of power system generator dynamics"--Abstract, page iii.

Advisor(s)

Venayagamoorthy, Ganesh K.

Committee Member(s)

Beetner, Daryl G.
Corzine, Keith, 1968-
Vojta, Thomas
Wunsch, Donald C.

Department(s)

Electrical and Computer Engineering

Degree Name

Ph. D. in Computer Engineering

Sponsor(s)

National Science Foundation (U.S.)
Missouri University of Science and Technology. Intelligent Systems Center
Real-Time Power and Intelligent Systems Laboratory

Publisher

Missouri University of Science and Technology

Publication Date

Fall 2011

Pagination

xiii, 109 pages

Note about bibliography

Includes bibliographical references (pages 105-108).

Rights

© 2011 Cameron Eric Johnson, All rights reserved.

Document Type

Dissertation - Restricted Access

File Type

text

Language

English

Library of Congress Subject Headings

Neural networks (Computer science)
Decoders (Electronics)
Synchronous data transmission systems

Thesis Number

T 10267

Print OCLC #

862973093

Electronic OCLC #

908695715

Link to Catalog Record

Electronic access to the full-text of this document is restricted to Missouri S&T users. Otherwise, request this publication directly from Missouri S&T Library or contact your local library.

http://laurel.lso.missouri.edu:80/record=b10158019~S5

Comments

Funded by GAANN [Graduate Assistance in Areas of National Need] grant P200A070504 and the National Science Foundation Emerging Frontiers in Research and Innovation grant 0836017

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