Nonlinear Neurodynamics Tool for System Analysis and Application for Time Series Prediction
Novel type of dynamical neural network, Freeman's K-models has been inspired by the biology and has been studied by researches in a number of research groups. This work is dedicated to providing a unifying test bed implementation that is capable of satisfying needs of possibly the most of these...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1016 |
|---|---|
| container_issue | |
| container_start_page | 1011 |
| container_title | |
| container_volume | 2 |
| creator | Beliaev, I. Ilin, R. Kozma, R. |
| description | Novel type of dynamical neural network, Freeman's K-models has been inspired by the biology and has been studied by researches in a number of research groups. This work is dedicated to providing a unifying test bed implementation that is capable of satisfying needs of possibly the most of these people. Given popularity of MATLABreg computational environment among engineers and scientists our effort is to develop a toolbox that would give way to simulate and experiment with the K-models in a simple manner as well as provide flexible tools for creation of applications that build on this dynamical model. The implementation is presented and detail of the design solutions are provided in this work. Also the successful usage of the toolbox is illustrated with the application to financial time series prediction by the K-models method |
| doi_str_mv | 10.1109/ICSMC.2005.1571278 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_1571278</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>1571278</ieee_id><sourcerecordid>1571278</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-8a6fc9488ca856f6326a1bcc9cf1807acf683f4d5f9d096b8a417aa6a907dda83</originalsourceid><addsrcrecordid>eNotkMlqwzAYhEUXaJrmBdqLXsCpJFvb0ZgugTQtOIVCD-GPFlCxLSOlB7990zYwMIf5GJhB6JaSJaVE36-a9qVZMkL4knJJmVRnaMa4lAUVnJ-jhZaKHFVqplV1gWaUCFZoxj6u0HXOX4QwUlE1Q5-bOHRhcJDwxn2naKcB-mAy3sbYYR8Tbqd8cD2uB-imHDKGweJ6HLtg4BDi8MdsQ-9w61JwGb8lZ4P5jW7QpYcuu8XJ5-j98WHbPBfr16dVU6-LQCU_FAqEN7pSyoDiwouSCaB7Y7Tx9DgCjBeq9JXlXluixV5BRSWAAE2ktaDKObr77w3Oud2YQg9p2p1-KX8AkjlWkw</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Nonlinear Neurodynamics Tool for System Analysis and Application for Time Series Prediction</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Beliaev, I. ; Ilin, R. ; Kozma, R.</creator><creatorcontrib>Beliaev, I. ; Ilin, R. ; Kozma, R.</creatorcontrib><description>Novel type of dynamical neural network, Freeman's K-models has been inspired by the biology and has been studied by researches in a number of research groups. This work is dedicated to providing a unifying test bed implementation that is capable of satisfying needs of possibly the most of these people. Given popularity of MATLABreg computational environment among engineers and scientists our effort is to develop a toolbox that would give way to simulate and experiment with the K-models in a simple manner as well as provide flexible tools for creation of applications that build on this dynamical model. The implementation is presented and detail of the design solutions are provided in this work. Also the successful usage of the toolbox is illustrated with the application to financial time series prediction by the K-models method</description><identifier>ISSN: 1062-922X</identifier><identifier>ISBN: 9780780392984</identifier><identifier>ISBN: 0780392981</identifier><identifier>EISSN: 2577-1655</identifier><identifier>DOI: 10.1109/ICSMC.2005.1571278</identifier><language>eng</language><publisher>IEEE</publisher><subject>Application software ; Biological system modeling ; Computational modeling ; Computer science ; Differential equations ; dynamical neural networks ; K-models ; Mathematical model ; Neural networks ; Neurodynamics ; Neurons ; Time series analysis ; time series prediction</subject><ispartof>2005 IEEE International Conference on Systems, Man and Cybernetics, 2005, Vol.2, p.1011-1016</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1571278$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,4050,4051,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1571278$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Beliaev, I.</creatorcontrib><creatorcontrib>Ilin, R.</creatorcontrib><creatorcontrib>Kozma, R.</creatorcontrib><title>Nonlinear Neurodynamics Tool for System Analysis and Application for Time Series Prediction</title><title>2005 IEEE International Conference on Systems, Man and Cybernetics</title><addtitle>ICSMC</addtitle><description>Novel type of dynamical neural network, Freeman's K-models has been inspired by the biology and has been studied by researches in a number of research groups. This work is dedicated to providing a unifying test bed implementation that is capable of satisfying needs of possibly the most of these people. Given popularity of MATLABreg computational environment among engineers and scientists our effort is to develop a toolbox that would give way to simulate and experiment with the K-models in a simple manner as well as provide flexible tools for creation of applications that build on this dynamical model. The implementation is presented and detail of the design solutions are provided in this work. Also the successful usage of the toolbox is illustrated with the application to financial time series prediction by the K-models method</description><subject>Application software</subject><subject>Biological system modeling</subject><subject>Computational modeling</subject><subject>Computer science</subject><subject>Differential equations</subject><subject>dynamical neural networks</subject><subject>K-models</subject><subject>Mathematical model</subject><subject>Neural networks</subject><subject>Neurodynamics</subject><subject>Neurons</subject><subject>Time series analysis</subject><subject>time series prediction</subject><issn>1062-922X</issn><issn>2577-1655</issn><isbn>9780780392984</isbn><isbn>0780392981</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotkMlqwzAYhEUXaJrmBdqLXsCpJFvb0ZgugTQtOIVCD-GPFlCxLSOlB7990zYwMIf5GJhB6JaSJaVE36-a9qVZMkL4knJJmVRnaMa4lAUVnJ-jhZaKHFVqplV1gWaUCFZoxj6u0HXOX4QwUlE1Q5-bOHRhcJDwxn2naKcB-mAy3sbYYR8Tbqd8cD2uB-imHDKGweJ6HLtg4BDi8MdsQ-9w61JwGb8lZ4P5jW7QpYcuu8XJ5-j98WHbPBfr16dVU6-LQCU_FAqEN7pSyoDiwouSCaB7Y7Tx9DgCjBeq9JXlXluixV5BRSWAAE2ktaDKObr77w3Oud2YQg9p2p1-KX8AkjlWkw</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Beliaev, I.</creator><creator>Ilin, R.</creator><creator>Kozma, R.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>2005</creationdate><title>Nonlinear Neurodynamics Tool for System Analysis and Application for Time Series Prediction</title><author>Beliaev, I. ; Ilin, R. ; Kozma, R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-8a6fc9488ca856f6326a1bcc9cf1807acf683f4d5f9d096b8a417aa6a907dda83</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Application software</topic><topic>Biological system modeling</topic><topic>Computational modeling</topic><topic>Computer science</topic><topic>Differential equations</topic><topic>dynamical neural networks</topic><topic>K-models</topic><topic>Mathematical model</topic><topic>Neural networks</topic><topic>Neurodynamics</topic><topic>Neurons</topic><topic>Time series analysis</topic><topic>time series prediction</topic><toplevel>online_resources</toplevel><creatorcontrib>Beliaev, I.</creatorcontrib><creatorcontrib>Ilin, R.</creatorcontrib><creatorcontrib>Kozma, R.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library Online</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Beliaev, I.</au><au>Ilin, R.</au><au>Kozma, R.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Nonlinear Neurodynamics Tool for System Analysis and Application for Time Series Prediction</atitle><btitle>2005 IEEE International Conference on Systems, Man and Cybernetics</btitle><stitle>ICSMC</stitle><date>2005</date><risdate>2005</risdate><volume>2</volume><spage>1011</spage><epage>1016</epage><pages>1011-1016</pages><issn>1062-922X</issn><eissn>2577-1655</eissn><isbn>9780780392984</isbn><isbn>0780392981</isbn><abstract>Novel type of dynamical neural network, Freeman's K-models has been inspired by the biology and has been studied by researches in a number of research groups. This work is dedicated to providing a unifying test bed implementation that is capable of satisfying needs of possibly the most of these people. Given popularity of MATLABreg computational environment among engineers and scientists our effort is to develop a toolbox that would give way to simulate and experiment with the K-models in a simple manner as well as provide flexible tools for creation of applications that build on this dynamical model. The implementation is presented and detail of the design solutions are provided in this work. Also the successful usage of the toolbox is illustrated with the application to financial time series prediction by the K-models method</abstract><pub>IEEE</pub><doi>10.1109/ICSMC.2005.1571278</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1062-922X |
| ispartof | 2005 IEEE International Conference on Systems, Man and Cybernetics, 2005, Vol.2, p.1011-1016 |
| issn | 1062-922X 2577-1655 |
| language | eng |
| recordid | cdi_ieee_primary_1571278 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Application software Biological system modeling Computational modeling Computer science Differential equations dynamical neural networks K-models Mathematical model Neural networks Neurodynamics Neurons Time series analysis time series prediction |
| title | Nonlinear Neurodynamics Tool for System Analysis and Application for Time Series Prediction |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T05%3A14%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Nonlinear%20Neurodynamics%20Tool%20for%20System%20Analysis%20and%20Application%20for%20Time%20Series%20Prediction&rft.btitle=2005%20IEEE%20International%20Conference%20on%20Systems,%20Man%20and%20Cybernetics&rft.au=Beliaev,%20I.&rft.date=2005&rft.volume=2&rft.spage=1011&rft.epage=1016&rft.pages=1011-1016&rft.issn=1062-922X&rft.eissn=2577-1655&rft.isbn=9780780392984&rft.isbn_list=0780392981&rft_id=info:doi/10.1109/ICSMC.2005.1571278&rft_dat=%3Cieee_6IE%3E1571278%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=1571278&rfr_iscdi=true |