Title page for ETD etd-0327103-141013

Type of Document Master's Thesis
Author Rajagopal, Pritam
URN etd-0327103-141013
Title Instantaneously Trained Neural Networks with Complex Inputs
Degree Master of Science in Electrical Engineering (M.S.E.E.)
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Subhash C. Kak Committee Chair
Ashok Srivastava Committee Member
Hsiao-Chun Wu Committee Member
  • complex input neural networks
Date of Defense 2003-03-11
Availability unrestricted
Neural network architectures that can handle complex inputs, such as backpropagation

networks, perceptrons or generalized Hopfield networks, require a large amount of time

and resources for the training process. This thesis adapts the time-efficient corner

classification approach to train feedforward neural networks to handle complex inputs

using prescriptive learning, where the network weights are assigned simply upon

examining the inputs. At first a straightforward generalization of the CC4 corner

classification algorithm is presented to highlight issues in training complex neural

networks. This algorithm performs poorly in a pattern classification experiment and for it

to perform well some inputs have to be restricted. This leads to the development of the

3C algorithm, which is the main contribution of the thesis. This algorithm is tested using

the pattern classification experiment and the results are found to be quite good. The

performance of the two algorithms in time series prediction is illustrated using the

Mackey-Glass time series. Quaternary input encoding is used for the pattern

classification and the time series prediction experiments since it reduces the network size

significantly by cutting down on the number of neurons required at the input layer.

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