"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.
Beetner, Daryl G.
Corzine, Keith, 1968-
Wunsch, Donald C.
Electrical and Computer Engineering
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
Intelligent Systems Center
Missouri University of Science and Technology
xiii, 109 pages
© 2011 Cameron Eric Johnson, All rights reserved.
Dissertation - Restricted Access
Neural networks (Computer science)
Synchronous data transmission systems
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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://merlin.lib.umsystem.edu:80/record=b10158019~S5
Johnson, Cameron Eric, "Spiking neural networks and their applications" (2011). Doctoral Dissertations. 78.
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