Genetic optimisation of control parameters of a neural network
One of the shortcomings of artificial neural networks (ANNs) is the difficulty in predicting the best control parameters for a certain application. The number of combinations of parameters is very large. This makes it very inefficient and expensive to search manually by trial and error. Genetic algo...
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| creator | Choi, B. Bluff, K. |
| description | One of the shortcomings of artificial neural networks (ANNs) is the difficulty in predicting the best control parameters for a certain application. The number of combinations of parameters is very large. This makes it very inefficient and expensive to search manually by trial and error. Genetic algorithms (GAs) are an excellent and effective search technique suitable for this task. This paper describes an investigation into the use of GAs to automate the choice of parameters in both a standard backpropagation (SBP) and a fuzzy backpropagation (FBP) network for different applications. |
| doi_str_mv | 10.1109/ANNES.1995.499466 |
| format | Conference Proceeding |
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The number of combinations of parameters is very large. This makes it very inefficient and expensive to search manually by trial and error. Genetic algorithms (GAs) are an excellent and effective search technique suitable for this task. 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The number of combinations of parameters is very large. This makes it very inefficient and expensive to search manually by trial and error. Genetic algorithms (GAs) are an excellent and effective search technique suitable for this task. This paper describes an investigation into the use of GAs to automate the choice of parameters in both a standard backpropagation (SBP) and a fuzzy backpropagation (FBP) network for different applications.</description><subject>Artificial neural networks</subject><subject>Frequency</subject><subject>Fuzzy sets</subject><subject>Genetic algorithms</subject><subject>Information technology</subject><subject>Neural networks</subject><subject>Optimal control</subject><subject>Pattern recognition</subject><subject>Space technology</subject><subject>Training data</subject><isbn>0818671742</isbn><isbn>9780818671746</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1995</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj81KAzEURgMiqLUPoKu8wIz5uZnkboRSaiuUurD7kplJIDozGZKI-PZW6tkcOIsPPkIeOKs5Z_i0Ohw27zVHVDUgQtNckTtmuGk01yBuyDLnD3YGlJJS35LnrZtcCR2NcwljyLaEONHoaRenkuJAZ5vs6IpL-a9aOrmvZIezyndMn_fk2tshu-W_F-T4sjmud9X-bfu6Xu2rYLBUAj3rBQBjWgvvpYZWKmyFUK3vmeXIWAeqhV5ZMAYQBBfIpWkRuLbg5YI8XmaDc-40pzDa9HO6HJS_06JFwQ</recordid><startdate>1995</startdate><enddate>1995</enddate><creator>Choi, B.</creator><creator>Bluff, K.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1995</creationdate><title>Genetic optimisation of control parameters of a neural network</title><author>Choi, B. ; Bluff, K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i89t-29f0d24400772ff374b359b225bfd0a1900c45b4d5a4884942129138b9417a4f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1995</creationdate><topic>Artificial neural networks</topic><topic>Frequency</topic><topic>Fuzzy sets</topic><topic>Genetic algorithms</topic><topic>Information technology</topic><topic>Neural networks</topic><topic>Optimal control</topic><topic>Pattern recognition</topic><topic>Space technology</topic><topic>Training data</topic><toplevel>online_resources</toplevel><creatorcontrib>Choi, B.</creatorcontrib><creatorcontrib>Bluff, K.</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>Choi, B.</au><au>Bluff, K.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Genetic optimisation of control parameters of a neural network</atitle><btitle>Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems</btitle><stitle>ANNES</stitle><date>1995</date><risdate>1995</risdate><spage>174</spage><epage>177</epage><pages>174-177</pages><isbn>0818671742</isbn><isbn>9780818671746</isbn><abstract>One of the shortcomings of artificial neural networks (ANNs) is the difficulty in predicting the best control parameters for a certain application. The number of combinations of parameters is very large. This makes it very inefficient and expensive to search manually by trial and error. Genetic algorithms (GAs) are an excellent and effective search technique suitable for this task. This paper describes an investigation into the use of GAs to automate the choice of parameters in both a standard backpropagation (SBP) and a fuzzy backpropagation (FBP) network for different applications.</abstract><pub>IEEE</pub><doi>10.1109/ANNES.1995.499466</doi><tpages>4</tpages></addata></record> |
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| identifier | ISBN: 0818671742 |
| ispartof | Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, 1995, p.174-177 |
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| language | eng |
| recordid | cdi_ieee_primary_499466 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Frequency Fuzzy sets Genetic algorithms Information technology Neural networks Optimal control Pattern recognition Space technology Training data |
| title | Genetic optimisation of control parameters of a neural network |
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