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Input Values Function for Improving Generalization Capability of BP Neural Network
As is known to all, Back propagation (BP) neural network has two important advantage and disadvantage: learning speed and generalization capability (GC). In this paper, we propose a new method by adding input values function (IVF) to improve the generalization of BP neural network. The result shows:...
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| creator | Tongzhi He Shijue Zheng Ping Zhang Ming Zou |
| description | As is known to all, Back propagation (BP) neural network has two important advantage and disadvantage: learning speed and generalization capability (GC). In this paper, we propose a new method by adding input values function (IVF) to improve the generalization of BP neural network. The result shows: GC in a certain extent has been improved throught this method. But if we want to do much more promoting in various fields, there are still a lot of things that we must to be studied. |
| doi_str_mv | 10.1109/APWCS.2010.64 |
| format | conference_proceeding |
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In this paper, we propose a new method by adding input values function (IVF) to improve the generalization of BP neural network. The result shows: GC in a certain extent has been improved throught this method. 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But if we want to do much more promoting in various fields, there are still a lot of things that we must to be studied.</description><subject>Algorithm design and analysis</subject><subject>BP neural network</subject><subject>Computer science</subject><subject>Education</subject><subject>Feedforward systems</subject><subject>Generalization Capability</subject><subject>Helium</subject><subject>Input Values Function</subject><subject>Multi-layer neural network</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>System testing</subject><subject>Wearable computers</subject><isbn>1424464676</isbn><isbn>9781424464678</isbn><isbn>1424464684</isbn><isbn>9781424464685</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpFjl1LwzAYhSMy0M1deuVN_sBmPt6ky-UsbhaGDh16OdL0jUS7tqStMn-9RQXPzcPhgcMh5JKzOefMXC-3L-nTXLChazghYw4CQINewOl_SfSIjAVjxgBwqc_ItG3f2BBQArg4J49Z1fQdfbZljy1d9ZXrQl1RX0eaHZpYf4Tqla6xwmjL8GV_ZGobm4cydEdae3qzpffYD3pA91nH9wsy8rZscfrHCdmtbnfp3WzzsM7S5WYWDOtm3AnnZILMF6bIXeGL3KJCpZw2Jh8OMq-0U9wrkHnhcyMscpkkMrFgHKKckKvf2YCI-yaGg43HvYIFU0zJbwUDUqA</recordid><startdate>201004</startdate><enddate>201004</enddate><creator>Tongzhi He</creator><creator>Shijue Zheng</creator><creator>Ping Zhang</creator><creator>Ming Zou</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201004</creationdate><title>Input Values Function for Improving Generalization Capability of BP Neural Network</title><author>Tongzhi He ; Shijue Zheng ; Ping Zhang ; Ming Zou</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-1c2cc37e0fd9dbcdfdbae5e55c699b0040f56c51f543bdfb92ae137737a49cee3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Algorithm design and analysis</topic><topic>BP neural network</topic><topic>Computer science</topic><topic>Education</topic><topic>Feedforward systems</topic><topic>Generalization Capability</topic><topic>Helium</topic><topic>Input Values Function</topic><topic>Multi-layer neural network</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>System testing</topic><topic>Wearable computers</topic><toplevel>online_resources</toplevel><creatorcontrib>Tongzhi He</creatorcontrib><creatorcontrib>Shijue Zheng</creatorcontrib><creatorcontrib>Ping Zhang</creatorcontrib><creatorcontrib>Ming Zou</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 Xplore</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>Tongzhi He</au><au>Shijue Zheng</au><au>Ping Zhang</au><au>Ming Zou</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Input Values Function for Improving Generalization Capability of BP Neural Network</atitle><btitle>2010 Asia-Pacific Conference on Wearable Computing Systems</btitle><stitle>APWCS</stitle><date>2010-04</date><risdate>2010</risdate><spage>228</spage><epage>231</epage><pages>228-231</pages><isbn>1424464676</isbn><isbn>9781424464678</isbn><eisbn>1424464684</eisbn><eisbn>9781424464685</eisbn><abstract>As is known to all, Back propagation (BP) neural network has two important advantage and disadvantage: learning speed and generalization capability (GC). In this paper, we propose a new method by adding input values function (IVF) to improve the generalization of BP neural network. The result shows: GC in a certain extent has been improved throught this method. But if we want to do much more promoting in various fields, there are still a lot of things that we must to be studied.</abstract><pub>IEEE</pub><doi>10.1109/APWCS.2010.64</doi><tpages>4</tpages></addata></record> |
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| ispartof | 2010 Asia-Pacific Conference on Wearable Computing Systems, 2010, p.228-231 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis BP neural network Computer science Education Feedforward systems Generalization Capability Helium Input Values Function Multi-layer neural network Neural networks Neurons System testing Wearable computers |
| title | Input Values Function for Improving Generalization Capability of BP Neural Network |
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