First Advisor

George G. Lendaris

Date of Publication

1991

Document Type

Thesis

Degree Name

Master of Science (M.S.) in Electrical and Computer Engineering

Department

Electrical Engineering

Language

English

Subjects

Neural networks (Computer science), Perceptrons, Pattern perception, Geometry -- Data processing

DOI

10.15760/etd.6126

Physical Description

1 online resource (93 p.)

Abstract

A new approach is proposed which uses a combination of a Backprop paradigm neural network along with some perceptron processing elements performing logic operations to construct a numeric-to-symbolic converter. The design approach proposed herein is capable of implementing a decision region defined by a multi-dimensional, non-linear boundary surface. By defining a "two-valued" subspace of the boundary surface, a Backprop paradigm neural network is used to model the boundary surf ace. An input vector is tested by the neural network boundary model (along with perceptron logic gates) to determine whether the incoming vector point is within the decision region or not. Experiments with two qualitatively different kinds of nonlinear surface were carried out to test and demonstrate the design approach.

Rights

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Comments

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Persistent Identifier

http://archives.pdx.edu/ds/psu/24617

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