Target recognition using fuzzy fusion classifier
This paper makes available a feature fusion technique in target recognition and proposes the necessary characteristics for fusion classifier design. Then, fuzzy fusion classifier (FFC) based on the characteristics presented is designed. FFC presented here does not make any assumption about samples d...
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| container_end_page | THC213 vol.2 |
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| container_start_page | THC2/9 |
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| container_volume | 2 |
| creator | Hou, P.K. Wang, X.J. Shi, X.Z. Lin, L.J. Zhang, M.Z. |
| description | This paper makes available a feature fusion technique in target recognition and proposes the necessary characteristics for fusion classifier design. Then, fuzzy fusion classifier (FFC) based on the characteristics presented is designed. FFC presented here does not make any assumption about samples distribution and emphasizes mutual restraints among different classes as well as synthesizes patterns like combination operation in fuzzy logic. For the application of underwater target recognition, FFC can efficiently improve classification performance of recognition system by synthesizing features of signals from multisensors. |
| doi_str_mv | 10.1109/IFIC.2000.859889 |
| format | Conference Proceeding |
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Then, fuzzy fusion classifier (FFC) based on the characteristics presented is designed. FFC presented here does not make any assumption about samples distribution and emphasizes mutual restraints among different classes as well as synthesizes patterns like combination operation in fuzzy logic. 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For the application of underwater target recognition, FFC can efficiently improve classification performance of recognition system by synthesizing features of signals from multisensors.</description><subject>Electric shock</subject><subject>Fuzzy neural networks</subject><subject>Fuzzy sets</subject><subject>Laboratories</subject><subject>Network synthesis</subject><subject>Neural networks</subject><subject>Sensor fusion</subject><subject>Sensor phenomena and characterization</subject><subject>Signal synthesis</subject><subject>Target recognition</subject><isbn>9782725700007</isbn><isbn>2725700000</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2000</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjz1rwzAURQWl0JJ6L538B-w-fUtjMUlrCGRo9iDJT0YldYrkDMmvT0K63HM5w4VLyCuFllKw7_2q71oGAK2R1hj7QCqrDdNM6qsE_USqUn5ulVtJlXgmsHV5xLnOGA7jlOZ0mOpjSdNYx-P5fLpmuamwd6WkmDC_kMfo9gWrfy7I92q57b6a9eaz7z7WTTJ2bpQXkoEKVEsaQSsQSrnBDdb7wKWWUbLBM-FNCB6RB0OBihgc44AOI1-Qt_tqQsTdX06_Lp9291P8As0IQqk</recordid><startdate>2000</startdate><enddate>2000</enddate><creator>Hou, P.K.</creator><creator>Wang, X.J.</creator><creator>Shi, X.Z.</creator><creator>Lin, L.J.</creator><creator>Zhang, M.Z.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2000</creationdate><title>Target recognition using fuzzy fusion classifier</title><author>Hou, P.K. ; Wang, X.J. ; Shi, X.Z. ; Lin, L.J. ; Zhang, M.Z.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i89t-6b45206c1751f0760466adad9bbc3575f52db24b8ccbee3c81014fca230eaef3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2000</creationdate><topic>Electric shock</topic><topic>Fuzzy neural networks</topic><topic>Fuzzy sets</topic><topic>Laboratories</topic><topic>Network synthesis</topic><topic>Neural networks</topic><topic>Sensor fusion</topic><topic>Sensor phenomena and characterization</topic><topic>Signal synthesis</topic><topic>Target recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Hou, P.K.</creatorcontrib><creatorcontrib>Wang, X.J.</creatorcontrib><creatorcontrib>Shi, X.Z.</creatorcontrib><creatorcontrib>Lin, L.J.</creatorcontrib><creatorcontrib>Zhang, M.Z.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hou, P.K.</au><au>Wang, X.J.</au><au>Shi, X.Z.</au><au>Lin, L.J.</au><au>Zhang, M.Z.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Target recognition using fuzzy fusion classifier</atitle><btitle>Proceedings of the Third International Conference on Information Fusion</btitle><stitle>IFIC</stitle><date>2000</date><risdate>2000</risdate><volume>2</volume><spage>THC2/9</spage><epage>THC213 vol.2</epage><pages>THC2/9-THC213 vol.2</pages><isbn>9782725700007</isbn><isbn>2725700000</isbn><abstract>This paper makes available a feature fusion technique in target recognition and proposes the necessary characteristics for fusion classifier design. Then, fuzzy fusion classifier (FFC) based on the characteristics presented is designed. FFC presented here does not make any assumption about samples distribution and emphasizes mutual restraints among different classes as well as synthesizes patterns like combination operation in fuzzy logic. For the application of underwater target recognition, FFC can efficiently improve classification performance of recognition system by synthesizing features of signals from multisensors.</abstract><pub>IEEE</pub><doi>10.1109/IFIC.2000.859889</doi></addata></record> |
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| language | eng |
| recordid | cdi_ieee_primary_859889 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Electric shock Fuzzy neural networks Fuzzy sets Laboratories Network synthesis Neural networks Sensor fusion Sensor phenomena and characterization Signal synthesis Target recognition |
| title | Target recognition using fuzzy fusion classifier |
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