Parallel implementation on improved error signal of backpropagation algorithm

The research work presented in this thesis is a continuation of Shamsuddin's work regarding proposed error signal for the backpropagation (BP) algorithm. The main focus is to parallelise Shamsuddin's work in order to improve the speedup of the BP algorithm. The experiments are implemen...

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Main Author:	Mohd Aris, Teh Noranis
Format:	Thesis
Language:	English
Published:	2001
Subjects:	Back propagation (Artificial intelligence) Parallel processing (Electronic computers) Error messages (Computer science)
Online Access:	http://psasir.upm.edu.my/id/eprint/8669/1/FSKTM_2001_10%20IR.pdf
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id	my-upm-ir.8669
record_format	uketd_dc
spelling	my-upm-ir.86692023-12-26T01:23:29Z Parallel implementation on improved error signal of backpropagation algorithm 2001-05 Mohd Aris, Teh Noranis The research work presented in this thesis is a continuation of Shamsuddin's work regarding proposed error signal for the backpropagation (BP) algorithm. The main focus is to parallelise Shamsuddin's work in order to improve the speedup of the BP algorithm. The experiments are implemented using the Sequent Symmetry SE30 parallel machine. The BP algorithm uses the data partitioning method with columnwise block striped and the batch mode weight updating strategy. Twenty-six patterns consisting of uppercase letters from 'A' to 'Z' are tested in the experiments. Two main factors taken into consideration in this, experiments are the execution time and speedup and the recognition rates. Shamsuddin's proposed BP parallel version, is compared with the sequential version. Experimental results shows that the execution time of the parallel version is much less than the execution time of the sequential version. The parallel version produces a good speedup as the number of processors, are increased due to the value that is near the ideal value. Experiments for testing the recognition rates involves the twenty-six trained sample data with perfect pattern and untrained sample data with 10% corrupted pattern. The recognition rates results show 100% accuracy for the trained and untrained data using the standard BP and Shamsuddin's proposed BP running sequentially. Back propagation (Artificial intelligence) Parallel processing (Electronic computers) Error messages (Computer science) 2001-05 Thesis http://psasir.upm.edu.my/id/eprint/8669/ http://psasir.upm.edu.my/id/eprint/8669/1/FSKTM_2001_10%20IR.pdf text en public masters Universiti Putra Malaysia Back propagation (Artificial intelligence) Parallel processing (Electronic computers) Error messages (Computer science) Faculty of Computer Science and Information Technology Mohd Saman, Md. Yazid
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
advisor	Mohd Saman, Md. Yazid
topic	Back propagation (Artificial intelligence) Parallel processing (Electronic computers) Error messages (Computer science)
spellingShingle	Back propagation (Artificial intelligence) Parallel processing (Electronic computers) Error messages (Computer science) Mohd Aris, Teh Noranis Parallel implementation on improved error signal of backpropagation algorithm
description	The research work presented in this thesis is a continuation of Shamsuddin's work regarding proposed error signal for the backpropagation (BP) algorithm. The main focus is to parallelise Shamsuddin's work in order to improve the speedup of the BP algorithm. The experiments are implemented using the Sequent Symmetry SE30 parallel machine. The BP algorithm uses the data partitioning method with columnwise block striped and the batch mode weight updating strategy. Twenty-six patterns consisting of uppercase letters from 'A' to 'Z' are tested in the experiments. Two main factors taken into consideration in this, experiments are the execution time and speedup and the recognition rates. Shamsuddin's proposed BP parallel version, is compared with the sequential version. Experimental results shows that the execution time of the parallel version is much less than the execution time of the sequential version. The parallel version produces a good speedup as the number of processors, are increased due to the value that is near the ideal value. Experiments for testing the recognition rates involves the twenty-six trained sample data with perfect pattern and untrained sample data with 10% corrupted pattern. The recognition rates results show 100% accuracy for the trained and untrained data using the standard BP and Shamsuddin's proposed BP running sequentially.
format	Thesis
qualification_level	Master's degree
author	Mohd Aris, Teh Noranis
author_facet	Mohd Aris, Teh Noranis
author_sort	Mohd Aris, Teh Noranis
title	Parallel implementation on improved error signal of backpropagation algorithm
title_short	Parallel implementation on improved error signal of backpropagation algorithm
title_full	Parallel implementation on improved error signal of backpropagation algorithm
title_fullStr	Parallel implementation on improved error signal of backpropagation algorithm
title_full_unstemmed	Parallel implementation on improved error signal of backpropagation algorithm
title_sort	parallel implementation on improved error signal of backpropagation algorithm
granting_institution	Universiti Putra Malaysia
granting_department	Faculty of Computer Science and Information Technology
publishDate	2001
url	http://psasir.upm.edu.my/id/eprint/8669/1/FSKTM_2001_10%20IR.pdf
_version_	1794018769874452480

Parallel implementation on improved error signal of backpropagation algorithm

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