Date of Award

1999

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Electrical and Computer Engineering

First Advisor

Subhash C. Kak

Abstract

Instantaneous learning is a desirable feature in neural networks. This type of learning enables the network to be trained very quickly, typically in just one or two passes of the training set as opposed to hundreds or even thousands of passes for networks trained by an iterative process, such as the error backpropagation (BP) algorithm. This dissertation first reviews several existing types of neural networks with instantaneous learning capability. It then proposes a new network called the Fuzzy Classification (FC) Neural Network that can be trained with two passes of the training samples. The first pass assigns the synaptic weights for the input and output layers. The second pass determines the radius of generalization r for each training sample. The network exhibits fuzziness in two regards: (1) by fuzzification of the location of each training vector in the input space; and (2) by assigning fuzzy memberships of output classes to new input vectors. The operation of the FC network is analyzed from different perspectives, namely, separability of patterns, curve fitting and kernel regression. It is shown that one mode of operation of the FC network can be made to behave like a Radial Basis Function (RBF) network by proper choice of membership function and network parameter. The performance of the FC network as a pattern classifier is also discussed in a statistical framework. The generalization performance of the FC network is compared against that of the CC4 Comer Classification Neural Network, the RBF network, and the Multilayer Perceptron network trained by the BP algorithm. Experiments involving a Henon map time series, a Mackey-Glass time series, and a spiral pattern are used in the comparison. The performance of the FC network is found to be comparable to that of RBF and BP networks, and better than that of the CC4 network. Its performance is also more scalable than those of CC4 and RBF networks. Further, the time taken to design an FC network for any given problem is much shorter compared to the other three networks.

ISBN

9780599636460

Pages

130

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