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PRGMDH algorithm for neural network development and its applications

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Date Issued:
2006
Summary:
The existing Group Method of Data Handling (GMDH) algorithm has characteristics that are ideal for neural network design. This thesis introduces a new algorithm that applies some of the best characteristics of GMDH to neural network design and develops a Pruning based Regenerated Network by discarding the neurons in a layer which don't contribute for the creation of neurons in next layer. Unlike other conventional algorithms, which generate a network which is a black box, the new algorithm provides visualization of the network displaying all the neurons in the network. The algorithm is general enough that it will accept any number of inputs and any sized training set. To show the flexibility of the Pruning based Regenerated Network, this algorithm is used to analyze different combinations of drugs and determine which pathways in these networks interact and determine the combination of drugs that take advantage of these interactions to maximize a desired effect on genes.
Title: PRGMDH algorithm for neural network development and its applications.
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Name(s): Tangadpelli, Chetan.
Florida Atlantic University, Degree grantor
Pandya, Abhijit S., Thesis advisor
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 2006
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 132 p.
Language(s): English
Summary: The existing Group Method of Data Handling (GMDH) algorithm has characteristics that are ideal for neural network design. This thesis introduces a new algorithm that applies some of the best characteristics of GMDH to neural network design and develops a Pruning based Regenerated Network by discarding the neurons in a layer which don't contribute for the creation of neurons in next layer. Unlike other conventional algorithms, which generate a network which is a black box, the new algorithm provides visualization of the network displaying all the neurons in the network. The algorithm is general enough that it will accept any number of inputs and any sized training set. To show the flexibility of the Pruning based Regenerated Network, this algorithm is used to analyze different combinations of drugs and determine which pathways in these networks interact and determine the combination of drugs that take advantage of these interactions to maximize a desired effect on genes.
Identifier: 9780542787843 (isbn), 13397 (digitool), FADT13397 (IID), fau:10247 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (M.S.)--Florida Atlantic University, 2006.
Subject(s): Neural networks (Computer science)
GMDH algorithms
Pattern recognition systems
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/13397
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.