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Neural network simulation (character recognition) using mathematica / Khairul Anuar Muhammmad

In this project Backpropogation technique has been chosen to train data and to test the data. This technique is selected because it is the most common technique in Artificiai Neural Network simuïation. The studies that had been carried out ïn this project is to simulate neural network using BPN (Bac...

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Main Author: Muhammmad, Khairul Anuar
Format: Thesis
Language:English
Published: 1998
Online Access:https://ir.uitm.edu.my/id/eprint/103198/1/103198.pdf
https://ir.uitm.edu.my/id/eprint/103198/
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spelling my.uitm.ir.1031982024-09-28T15:14:20Z https://ir.uitm.edu.my/id/eprint/103198/ Neural network simulation (character recognition) using mathematica / Khairul Anuar Muhammmad Muhammmad, Khairul Anuar In this project Backpropogation technique has been chosen to train data and to test the data. This technique is selected because it is the most common technique in Artificiai Neural Network simuïation. The studies that had been carried out ïn this project is to simulate neural network using BPN (Backpropagation network) to recognize the capital letters and numbers. The BPN is a iayered, feedforward that is fully interconnected by layers. There is no feedback connections and no connections that bypass one tayer to go directly to a later ïayer. Because it is so powerful, the backpropagation network has become an industry Standard. Among the advantages of backprop are its abiiity to store rmmbers of pattems far in excess of its built-in vector dimensionality. The network sometimes may fait when trying to solve real problems, where it fail to converge after a large number of training set. When this phenomenon occurs, changes has to be made by adjusting the weight initiahzation, learning rate, and adding extra parameter such as momentum. 1998 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/103198/1/103198.pdf Neural network simulation (character recognition) using mathematica / Khairul Anuar Muhammmad. (1998) Degree thesis, thesis, Universiti Teknologi MARA.
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
description In this project Backpropogation technique has been chosen to train data and to test the data. This technique is selected because it is the most common technique in Artificiai Neural Network simuïation. The studies that had been carried out ïn this project is to simulate neural network using BPN (Backpropagation network) to recognize the capital letters and numbers. The BPN is a iayered, feedforward that is fully interconnected by layers. There is no feedback connections and no connections that bypass one tayer to go directly to a later ïayer. Because it is so powerful, the backpropagation network has become an industry Standard. Among the advantages of backprop are its abiiity to store rmmbers of pattems far in excess of its built-in vector dimensionality. The network sometimes may fait when trying to solve real problems, where it fail to converge after a large number of training set. When this phenomenon occurs, changes has to be made by adjusting the weight initiahzation, learning rate, and adding extra parameter such as momentum.
format Thesis
author Muhammmad, Khairul Anuar
spellingShingle Muhammmad, Khairul Anuar
Neural network simulation (character recognition) using mathematica / Khairul Anuar Muhammmad
author_facet Muhammmad, Khairul Anuar
author_sort Muhammmad, Khairul Anuar
title Neural network simulation (character recognition) using mathematica / Khairul Anuar Muhammmad
title_short Neural network simulation (character recognition) using mathematica / Khairul Anuar Muhammmad
title_full Neural network simulation (character recognition) using mathematica / Khairul Anuar Muhammmad
title_fullStr Neural network simulation (character recognition) using mathematica / Khairul Anuar Muhammmad
title_full_unstemmed Neural network simulation (character recognition) using mathematica / Khairul Anuar Muhammmad
title_sort neural network simulation (character recognition) using mathematica / khairul anuar muhammmad
publishDate 1998
url https://ir.uitm.edu.my/id/eprint/103198/1/103198.pdf
https://ir.uitm.edu.my/id/eprint/103198/
_version_ 1811598311502643200
score 13.211869

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