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Handprinted character recognition and Alopex algorithm analysis

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Date Issued:
1994
Summary:
A novel neural network, trained with the Alopex algorithm to recognize handprinted characters, was developed in this research. It was constructed by an encoded fully connected multi-layer perceptron (EFCMP). It consists of one input layer, one intermediate layer, and one encoded output layer. The Alopex algorithm is used to supervise the training of the EFCMP. Alopex is a stochastic algorithm used to solve optimization problems. The Alopex algorithm has been shown to accelerate the rate of convergence in the training procedure. Software simulation programs were developed for training, testing and analyzing the performance of this EFCMP architecture. Several neural networks with different structures were developed and compared. Optimization of the Alopex algorithm was explored through simulations of the EFCMP training procedure with the use of different parametric values for Alopex.
Title: Handprinted character recognition and Alopex algorithm analysis.
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Name(s): Du, Jian.
Florida Atlantic University, Degree grantor
Shankar, Ravi, Thesis advisor
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 1994
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 128 p.
Language(s): English
Summary: A novel neural network, trained with the Alopex algorithm to recognize handprinted characters, was developed in this research. It was constructed by an encoded fully connected multi-layer perceptron (EFCMP). It consists of one input layer, one intermediate layer, and one encoded output layer. The Alopex algorithm is used to supervise the training of the EFCMP. Alopex is a stochastic algorithm used to solve optimization problems. The Alopex algorithm has been shown to accelerate the rate of convergence in the training procedure. Software simulation programs were developed for training, testing and analyzing the performance of this EFCMP architecture. Several neural networks with different structures were developed and compared. Optimization of the Alopex algorithm was explored through simulations of the EFCMP training procedure with the use of different parametric values for Alopex.
Identifier: 15012 (digitool), FADT15012 (IID), fau:11790 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (M.S.E.)--Florida Atlantic University, 1994.
Subject(s): Algorithms
Neural networks (Computer science)
Optical character recognition devices
Writing--Data processing
Image processing
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/15012
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.