Graduation Semester and Year

2012

Language

English

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering

Department

Electrical Engineering

First Advisor

Michael T Manry

Abstract

This thesis proposes a novel approach for designing a neural network based forecaster that predicts more than one variable at a time. A second order two stage neural network training algorithm is used that employs orthogonal least square for training the output weights. In order to reduce the size of the network and train the forecaster optimally it uses time-domain feature selection and KLT transform based feature selection. The forecaster works well and the feature selection reduces the number of required inputs on the order of 70 %.

Disciplines

Electrical and Computer Engineering | Engineering

Comments

Degree granted by The University of Texas at Arlington

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