Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Applied Physics | en_US |
| dc.creator | Ho, Wai-shing | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/2177 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Approximation of a fractal curve using feed-forward neural networks | en_US |
| dcterms.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. | en_US |
| dcterms.extent | 36 leaves : ill. ; 30 cm | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2000 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Phil. | en_US |
| dcterms.LCSH | Neural networks (Computer science) | en_US |
| dcterms.LCSH | Fractals | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b15249141.pdf | For All Users | 1.63 MB | Adobe PDF | View/Open |
Copyright Undertaking
As a bona fide Library user, I declare that:
- I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
- I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
- I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.
Please use this identifier to cite or link to this item:
https://theses.lib.polyu.edu.hk/handle/200/2177