Date of Award

Spring 1995

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical & Computer Engineering

Program/Concentration

Electrical Engineering

Committee Director

Oscar R. Gonzalez

Committee Member

Joseph L. Hibey

Committee Member

John W. Stoughton

Call Number for Print

Special Collections LD4331.E55 R44

Abstract

In this thesis a procedure to design multilayer feedforward networks for system identification with good prediction properties is presented. Central to the design procedure is a means to characterize the prediction capabilities of various trained neural networks. Such knowledge will allow for the identification of the best network design. For system identification purposes, a "good" model is one that is good at predicting, In particular, a good model is one that produces small prediction errors when applied to a set of cross-validation data. We formulate and implement a criterion function designed to measure the size of a trained neural network's prediction error. The criterion function or generalization metric is implemented in three system identification design examples. The metric is used to determine the number of delays needed for the input signal, the number of hidden nodes; and the number of training cycles necessary to train the neural network.

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DOI

10.25777/wy5f-ed63

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