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Estimation of Internet transit times using a fast-computing artificial neural network (FC-ANN)
- Date Issued:
- 2001
- Summary:
- The objective of this research is to determine the macroscopic behavior of packet transit-times across the global Internet cloud using an artificial neural network (ANN). Specifically, the problem addressed here refers to using a "fast-convergent" ANN for the purpose indicated. The underlying principle of fast-convergence is that, the data presented in training and prediction modes of the ANN is in the entropy (information-theoretic) domain, and the associated annealing process is "tuned" to adopt only the useful information content and discard the posentropy part of the data presented. To demonstrate the efficacy of the research pursued, a feedforward ANN structure is developed and the necessary transformations required to convert the input data from the parametric-domain to the entropy-domain (and a corresponding inverse transformation) are followed so as to retrieve the output in parametric-domain. The fast-convergent or fast-computing ANN (FC-ANN) developed is deployed to predict the packet-transit performance across the Internet. (Abstract shortened by UMI.)
Title: | Estimation of Internet transit times using a fast-computing artificial neural network (FC-ANN). |
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Name(s): |
Fasulo, Joseph V. Florida Atlantic University, Degree grantor Neelakanta, Perambur S., Thesis advisor |
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Type of Resource: | text | |
Genre: | Electronic Thesis Or Dissertation | |
Issuance: | monographic | |
Date Issued: | 2001 | |
Publisher: | Florida Atlantic University | |
Place of Publication: | Boca Raton, Fla. | |
Physical Form: | application/pdf | |
Extent: | 117 p. | |
Language(s): | English | |
Summary: | The objective of this research is to determine the macroscopic behavior of packet transit-times across the global Internet cloud using an artificial neural network (ANN). Specifically, the problem addressed here refers to using a "fast-convergent" ANN for the purpose indicated. The underlying principle of fast-convergence is that, the data presented in training and prediction modes of the ANN is in the entropy (information-theoretic) domain, and the associated annealing process is "tuned" to adopt only the useful information content and discard the posentropy part of the data presented. To demonstrate the efficacy of the research pursued, a feedforward ANN structure is developed and the necessary transformations required to convert the input data from the parametric-domain to the entropy-domain (and a corresponding inverse transformation) are followed so as to retrieve the output in parametric-domain. The fast-convergent or fast-computing ANN (FC-ANN) developed is deployed to predict the packet-transit performance across the Internet. (Abstract shortened by UMI.) | |
Identifier: | 9780493407180 (isbn), 12835 (digitool), FADT12835 (IID), fau:9710 (fedora) | |
Collection: | FAU Electronic Theses and Dissertations Collection | |
Note(s): |
College of Engineering and Computer Science Thesis (M.S.)--Florida Atlantic University, 2001. |
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Subject(s): |
Neural networks (Computer science) Information theory Packet switching (Data transmission) Internet |
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Held by: | Florida Atlantic University Libraries | |
Persistent Link to This Record: | http://purl.flvc.org/fcla/dt/12835 | |
Sublocation: | Digital Library | |
Use and Reproduction: | Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder. | |
Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
Host Institution: | FAU | |
Is Part of Series: | Florida Atlantic University Digital Library Collections. |