Doctoral Dissertations


"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.


Venayagamoorthy, Ganesh K.

Committee Member(s)

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


Electrical and Computer Engineering

Degree Name

Ph. D. in Computer Engineering


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


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

Research Center/Lab(s)

Intelligent Systems Center


Missouri University of Science and Technology

Publication Date

Fall 2011


xiii, 109 pages

Note about bibliography

Includes bibliographical references (pages 105-108).


© 2011 Cameron Eric Johnson, All rights reserved.

Document Type

Dissertation - Open Access

File Type




Subject Headings

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

Thesis Number

T 10267

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