Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/86397
Title: Approximation of a fractal curve using feed-forward neural networks
Authors: Ho, Wai-shing
Degree: M.Phil.
Issue Date: 2000
Abstract: The approximation of fractal curves in the form of Brownian functions by two-layer feed-forward neural networks is studied. The network parameters are restricted within a finite range. For given realizations of the Brownian target function, all local minima in the output error measure with appreciable sizes of basins of attraction are located and found to be about a dozen in number in each case. The error follows a log-normal distribution which can be explained by a distribution of mean squared normal deviates. Its mean value exhibits simple scaling relationships with the number of hidden neurons and the number of training patterns. Our numerical findings are explained by comparison with a simple piecewise linear fit approach.
Subjects: Neural networks (Computer science)
Fractals
Hong Kong Polytechnic University -- Dissertations
Pages: 36 leaves : ill. ; 30 cm
Appears in Collections:Thesis

Show full item record

Page views

4
Citations as of May 22, 2022

Google ScholarTM

Check


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