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.
Rights
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DOI
10.25777/wy5f-ed63
Recommended Citation
Reeves, Denise M..
"Generalization Metrics for Neural Modeling Applications in System Identification"
(1995). Master of Science (MS), Thesis, Electrical & Computer Engineering, Old Dominion University, DOI: 10.25777/wy5f-ed63
https://digitalcommons.odu.edu/ece_etds/490
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Computational Engineering Commons, Computer Engineering Commons, Computer Sciences Commons