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		<identifier>oai:zbc.uz.zgora.pl:86283</identifier>
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<dc:title xml:lang="pl"><![CDATA[Forecasting models for chaotic fractional-order oscillators using neural networks]]></dc:title>
<dc:creator><![CDATA[Bingi, Kishore]]></dc:creator>
<dc:creator><![CDATA[Prusty, Rajanarayan B.]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[chaotic oscillators]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[data-driven forecasting]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[fractional-order systems]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[model-free analysis]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[neural networks]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[time-series prediction]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[This paper proposes novel forecasting models for fractional-order chaotic oscillators, such as Duffing`s, Van der Pol`s, Tamaševičius`s and Chua`s, using feedforward neural networks. The models predict a change in the state values which bears a weighted relationship with the oscillator states. Such an arrangement is a suitable candidate model for out-of-sample forecasting of system states. The proposed neural network-assisted weighted model is applied to the above oscillators.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[The improved out-of-sample forecasting results of the proposed modeling strategy compared with the literature are comprehensively analyzed. The proposed models corresponding to the optimal weights result in the least mean square error (MSE) for all the system states. Further, the MSE for the proposed model is less in most of the oscillators compared with the one reported in the literature. The proposed prediction model`s out-of-sample forecasting plots show the best tracking ability to approximate future state values.]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Uniwersytet Zielonogórski]]></dc:publisher>
<dc:contributor><![CDATA[Korbicz, Józef (1951- ) - red.]]></dc:contributor>
<dc:contributor><![CDATA[Uciński, Dariusz - red.]]></dc:contributor>
<dc:date><![CDATA[2021]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/repozytorium/Content/86283/AMCS_2021_31_3_2.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/repozytorium/dlibra/publication/101376/edition/86283/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:86283]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[AMCS, volume 31, number 3 (2021)]]></dc:source>
<dc:source xml:lang="pl"><![CDATA[https://www.amcs.uz.zgora.pl/?action=papers&issue=121]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:101376]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
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