Classifiers for sonar target differentiation
In this paper, the processing of sonar signals has been carried out using Minimal Resource Allocation Network (MRAN), Probabilistic Neural Network (PNN) and Fuzzy Artmap (FAM) in differentiation of commonly encountered features in indoor environments. The stability-plasticity behaviors of all three...
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| Format: | Article |
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2004
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| Online Access: | http://shdl.mmu.edu.my/2499/ |
| _version_ | 1848790071283548160 |
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| author | Loo, , CK Rao, , MVC Lim, , WS |
| author_facet | Loo, , CK Rao, , MVC Lim, , WS |
| author_sort | Loo, , CK |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | In this paper, the processing of sonar signals has been carried out using Minimal Resource Allocation Network (MRAN), Probabilistic Neural Network (PNN) and Fuzzy Artmap (FAM) in differentiation of commonly encountered features in indoor environments. The stability-plasticity behaviors of all three networks have been investigated. The experimental result shows that MRAN possesses lower network complexity but experiences higher plasticity in comparison to PNN and FAM. The study also shows that MRAN performance is superior in terms of on-line learning than PNN and FAM. |
| first_indexed | 2025-11-14T18:06:47Z |
| format | Article |
| id | mmu-2499 |
| institution | Multimedia University |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:06:47Z |
| publishDate | 2004 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-24992011-08-22T02:40:46Z http://shdl.mmu.edu.my/2499/ Classifiers for sonar target differentiation Loo, , CK Rao, , MVC Lim, , WS QA75.5-76.95 Electronic computers. Computer science In this paper, the processing of sonar signals has been carried out using Minimal Resource Allocation Network (MRAN), Probabilistic Neural Network (PNN) and Fuzzy Artmap (FAM) in differentiation of commonly encountered features in indoor environments. The stability-plasticity behaviors of all three networks have been investigated. The experimental result shows that MRAN possesses lower network complexity but experiences higher plasticity in comparison to PNN and FAM. The study also shows that MRAN performance is superior in terms of on-line learning than PNN and FAM. 2004 Article NonPeerReviewed Loo, , CK and Rao, , MVC and Lim, , WS (2004) Classifiers for sonar target differentiation. KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 2, PROCEEDINGS , 3214 . pp. 305-311. ISSN 0302-9743 |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Loo, , CK Rao, , MVC Lim, , WS Classifiers for sonar target differentiation |
| title | Classifiers for sonar target differentiation |
| title_full | Classifiers for sonar target differentiation |
| title_fullStr | Classifiers for sonar target differentiation |
| title_full_unstemmed | Classifiers for sonar target differentiation |
| title_short | Classifiers for sonar target differentiation |
| title_sort | classifiers for sonar target differentiation |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://shdl.mmu.edu.my/2499/ |