Please use this identifier to cite or link to this item:

Function approximation using neural networks : a simulation study

File Description SizeFormat 
uhm_phd_9300336_r.pdfVersion for non-UH users. Copying/Printing is not permitted4.16 MBAdobe PDFView/Open
uhm_phd_9300336_uh.pdfVersion for UH users4.11 MBAdobe PDFView/Open

Item Summary

Title: Function approximation using neural networks : a simulation study
Authors: Marquez, Leorey O.
Issue Date: 1992
Abstract: This dissertation analyzes the effect of different characteristics of data on the training and estimation accuracy of neural networks. The literature on the universal approximation property of neural networks is reviewed. An examination of the relationship of the neural network approach to traditional statistical methods of approximation brought about proposed enhancements to the neural network training procedure. The study generated data samples characterized by different functional forms, levels of random noise, number and magnitude of outliers, and strength of multicollinearity. These samples were then used to train a neural network. The accuracy of the neural network estimate was tested and compared with the accuracy of the estimates obtained from the true model and those from Specht's GRNN model. Statistics on the length of training and the complexity of the neural network estimate were also collected and analyzed.
Description: Thesis (Ph. D.)--University of Hawaii at Manoa, 1992.
Includes bibliographical references (leaves 144-148)
xiii, 148 leaves, bound ill. 29 cm
Rights: All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
Appears in Collections:Ph.D. - Communication and Information Sciences

Items in ScholarSpace are protected by copyright, with all rights reserved, unless otherwise indicated.