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....

全面介紹

Saved in:
書目詳細資料
主要作者: Mutter, Kussay Nugamesh
格式: Thesis
語言:English
出版: 2010
主題:
在線閱讀:http://eprints.usm.my/41915/1/KUSSAY_NUGAMESH_MUTTER.pdf
標簽: 添加標簽
沒有標簽, 成為第一個標記此記錄!
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