Chaotic behavior in a learning model
A family of applications that appears when a discretized model of differential equations that simulates the learning process as response to some stimuli is presented. This discrete iterative model has been analyzed in the one-dimensional case and its chaotic behavior has been also studied. Furthermo...
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| Veröffentlicht in: | Nonlinear analysis: real world applications 2010-02, Vol.11 (1), p.414-422 |
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| container_title | Nonlinear analysis: real world applications |
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| creator | Grau-Sánchez, M. Noguera, M. Peris, J.M. Díaz-Barrero, J.L. |
| description | A family of applications that appears when a discretized model of differential equations that simulates the learning process as response to some stimuli is presented. This discrete iterative model has been analyzed in the one-dimensional case and its chaotic behavior has been also studied. Furthermore, the results computed from the simulations carried out with the general
n
-dimensional learning model have been compared with those of the uniparametric family and almost complete agreement has been obtained. |
| doi_str_mv | 10.1016/j.nonrwa.2008.11.013 |
| format | Article |
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-dimensional learning model have been compared with those of the uniparametric family and almost complete agreement has been obtained.</description><identifier>ISSN: 1468-1218</identifier><identifier>EISSN: 1878-5719</identifier><identifier>DOI: 10.1016/j.nonrwa.2008.11.013</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>[formula omitted]-unimodal application ; Associative learning ; Chaos ; Chaos theory ; Computation ; Computer simulation ; Differential equations ; Iterative methods ; Learning ; Nonlinearity ; Sensitivity to initial conditions ; Stimuli ; Topological transitivity</subject><ispartof>Nonlinear analysis: real world applications, 2010-02, Vol.11 (1), p.414-422</ispartof><rights>2008 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c338t-6cc2dc62ee6c97dd6c23f8bfccb783ee4834af171243a0db306d30888cf083303</citedby><cites>FETCH-LOGICAL-c338t-6cc2dc62ee6c97dd6c23f8bfccb783ee4834af171243a0db306d30888cf083303</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.nonrwa.2008.11.013$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Grau-Sánchez, M.</creatorcontrib><creatorcontrib>Noguera, M.</creatorcontrib><creatorcontrib>Peris, J.M.</creatorcontrib><creatorcontrib>Díaz-Barrero, J.L.</creatorcontrib><title>Chaotic behavior in a learning model</title><title>Nonlinear analysis: real world applications</title><description>A family of applications that appears when a discretized model of differential equations that simulates the learning process as response to some stimuli is presented. This discrete iterative model has been analyzed in the one-dimensional case and its chaotic behavior has been also studied. Furthermore, the results computed from the simulations carried out with the general
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-dimensional learning model have been compared with those of the uniparametric family and almost complete agreement has been obtained.</description><subject>[formula omitted]-unimodal application</subject><subject>Associative learning</subject><subject>Chaos</subject><subject>Chaos theory</subject><subject>Computation</subject><subject>Computer simulation</subject><subject>Differential equations</subject><subject>Iterative methods</subject><subject>Learning</subject><subject>Nonlinearity</subject><subject>Sensitivity to initial conditions</subject><subject>Stimuli</subject><subject>Topological transitivity</subject><issn>1468-1218</issn><issn>1878-5719</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><recordid>eNp9kM1OwzAQhC0EEqXwBhxyQOKU4I1Tx70goYg_qRIXOFvOekNdpXGx0yLeHlfhzGnnMDOr-Ri7Bl4AB3m3KQY_hG9TlJyrAqDgIE7YDFSt8kUNy9OkK6lyKEGds4sYN5xDDQJm7KZZGz86zFpam4PzIXNDZrKeTBjc8JltvaX-kp11po909Xfn7OPp8b15yVdvz6_NwypHIdSYS8TSoiyJJC5rayWWolNth9jWShBVSlSmS4_LShhuW8GlFVwphR1XQnAxZ7dT7y74rz3FUW9dROp7M5DfR60WaQ0XdZWc1eTE4GMM1OldcFsTfjRwfWSiN3pioo9MNIBOTFLsfopRWnFwFHRERwOSdYFw1Na7_wt-ASYyazc</recordid><startdate>20100201</startdate><enddate>20100201</enddate><creator>Grau-Sánchez, M.</creator><creator>Noguera, M.</creator><creator>Peris, J.M.</creator><creator>Díaz-Barrero, J.L.</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20100201</creationdate><title>Chaotic behavior in a learning model</title><author>Grau-Sánchez, M. ; Noguera, M. ; Peris, J.M. ; Díaz-Barrero, J.L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c338t-6cc2dc62ee6c97dd6c23f8bfccb783ee4834af171243a0db306d30888cf083303</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>[formula omitted]-unimodal application</topic><topic>Associative learning</topic><topic>Chaos</topic><topic>Chaos theory</topic><topic>Computation</topic><topic>Computer simulation</topic><topic>Differential equations</topic><topic>Iterative methods</topic><topic>Learning</topic><topic>Nonlinearity</topic><topic>Sensitivity to initial conditions</topic><topic>Stimuli</topic><topic>Topological transitivity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Grau-Sánchez, M.</creatorcontrib><creatorcontrib>Noguera, M.</creatorcontrib><creatorcontrib>Peris, J.M.</creatorcontrib><creatorcontrib>Díaz-Barrero, J.L.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Nonlinear analysis: real world applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Grau-Sánchez, M.</au><au>Noguera, M.</au><au>Peris, J.M.</au><au>Díaz-Barrero, J.L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Chaotic behavior in a learning model</atitle><jtitle>Nonlinear analysis: real world applications</jtitle><date>2010-02-01</date><risdate>2010</risdate><volume>11</volume><issue>1</issue><spage>414</spage><epage>422</epage><pages>414-422</pages><issn>1468-1218</issn><eissn>1878-5719</eissn><abstract>A family of applications that appears when a discretized model of differential equations that simulates the learning process as response to some stimuli is presented. This discrete iterative model has been analyzed in the one-dimensional case and its chaotic behavior has been also studied. Furthermore, the results computed from the simulations carried out with the general
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| ispartof | Nonlinear analysis: real world applications, 2010-02, Vol.11 (1), p.414-422 |
| issn | 1468-1218 1878-5719 |
| language | eng |
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| source | Elsevier ScienceDirect Journals Complete |
| subjects | [formula omitted]-unimodal application Associative learning Chaos Chaos theory Computation Computer simulation Differential equations Iterative methods Learning Nonlinearity Sensitivity to initial conditions Stimuli Topological transitivity |
| title | Chaotic behavior in a learning model |
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