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....
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| Format: | Thesis |
| Language: | English |
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2010
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| Online Access: | http://eprints.usm.my/41915/1/KUSSAY_NUGAMESH_MUTTER.pdf |
| _version_ | 1825834519807131648 |
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| author | Mutter, Kussay Nugamesh |
| author_facet | Mutter, Kussay Nugamesh |
| author_sort | Mutter, Kussay Nugamesh |
| collection | USM |
| 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. |
| first_indexed | 2024-03-06T15:23:58Z |
| format | Thesis |
| id | usm.eprints-41915 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2024-03-06T15:23:58Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | usm.eprints-419152019-04-12T05:26:59Z http://eprints.usm.my/41915/ Developing Hopfield Neural Network For Color Image Recognition 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 NonPeerReviewed application/pdf en http://eprints.usm.my/41915/1/KUSSAY_NUGAMESH_MUTTER.pdf Mutter, Kussay Nugamesh (2010) Developing Hopfield Neural Network For Color Image Recognition. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | QC1 Physics (General) Mutter, Kussay Nugamesh Developing Hopfield Neural Network For Color Image Recognition |
| title | 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_short | Developing Hopfield Neural Network For Color Image Recognition |
| title_sort | developing hopfield neural network for color image recognition |
| topic | QC1 Physics (General) |
| url | http://eprints.usm.my/41915/1/KUSSAY_NUGAMESH_MUTTER.pdf |
| work_keys_str_mv | AT mutterkussaynugamesh developinghopfieldneuralnetworkforcolorimagerecognition |