Developing Hopfield Neural Network For Color Image Recognition

Hopfield Neural Network (HNN) is an iterative auto-associative network which consists of a single layer of fully connected processing elements and converges to the nearest match vector. This network alters the input patterns through successive iterations until a learned vector evolves at the output....

Full description

Saved in:

Bibliographic Details
Main Author:	Mutter, Kussay Nugamesh
Format:	Thesis
Language:	English
Published:	2010
Subjects:	QC1 Physics (General)
Online Access:	http://eprints.usm.my/41915/1/KUSSAY_NUGAMESH_MUTTER.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-usm-ep.41915
record_format	uketd_dc
spelling	my-usm-ep.419152019-04-12T05:26:59Z Developing Hopfield Neural Network For Color Image Recognition 2010 Mutter, Kussay Nugamesh QC1 Physics (General) Hopfield Neural Network (HNN) is an iterative auto-associative network which consists of a single layer of fully connected processing elements and converges to the nearest match vector. This network alters the input patterns through successive iterations until a learned vector evolves at the output. Then the output will no longer change with successive iterations. HNN faces real problems when it deals with images of more than two colors, noisy convergence, limited capacity, and slow learning and converging according to the number of vectors and their sizes. These problems were studied and tested the proposed solutions to obtain the optimum performance of HNN and set a starting for future research. 2010 Thesis http://eprints.usm.my/41915/ http://eprints.usm.my/41915/1/KUSSAY_NUGAMESH_MUTTER.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Fizik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QC1 Physics (General)
spellingShingle	QC1 Physics (General) Mutter, Kussay Nugamesh Developing Hopfield Neural Network For Color Image Recognition
description	Hopfield Neural Network (HNN) is an iterative auto-associative network which consists of a single layer of fully connected processing elements and converges to the nearest match vector. This network alters the input patterns through successive iterations until a learned vector evolves at the output. Then the output will no longer change with successive iterations. HNN faces real problems when it deals with images of more than two colors, noisy convergence, limited capacity, and slow learning and converging according to the number of vectors and their sizes. These problems were studied and tested the proposed solutions to obtain the optimum performance of HNN and set a starting for future research.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mutter, Kussay Nugamesh
author_facet	Mutter, Kussay Nugamesh
author_sort	Mutter, Kussay Nugamesh
title	Developing Hopfield Neural Network For Color Image Recognition
title_short	Developing Hopfield Neural Network For Color Image Recognition
title_full	Developing Hopfield Neural Network For Color Image Recognition
title_fullStr	Developing Hopfield Neural Network For Color Image Recognition
title_full_unstemmed	Developing Hopfield Neural Network For Color Image Recognition
title_sort	developing hopfield neural network for color image recognition
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Fizik
publishDate	2010
url	http://eprints.usm.my/41915/1/KUSSAY_NUGAMESH_MUTTER.pdf
_version_	1747820995874390016

Developing Hopfield Neural Network For Color Image Recognition

Similar Items