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<dc:title xml:lang="pl"><![CDATA[Approximation of phenol concentration using novel hybrid computational intelligence methods]]></dc:title>
<dc:creator><![CDATA[Pławiak, Paweł]]></dc:creator>
<dc:creator><![CDATA[Tadeusiewicz, Ryszard (1947- )]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[soft computing]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[neural networks]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[genetic algorithms]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[fuzzy systems]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[evolutionary-neural systems]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[pattern recognition]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[chemometrics]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[This paper presents two innovative evolutionary-neural systems based on feed-forward and recurrent neural networks used for quantitative analysis. These systems have been applied for approximation of phenol concentration. Their performance was compared against the conventional methods of artificial intelligence (artificial neural networks, fuzzy logic and genetic algorithms).]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[The proposed systems are a combination of data preprocessing methods, genetic algorithms and the Levenberg?Marquardt (LM) algorithm used for learning feed forward and recurrent neural networks. The initial weights and biases of neural networks chosen by the use of a genetic algorithm are then tuned with an LM algorithm. The evaluation is made on the basis of accuracy and complexity criteria. The main advantage of proposed systems is the elimination of random selection of the network weights and biases, resulting in increased efficiency of the systems.]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Uniwersytet Zielonogórski]]></dc:publisher>
<dc:contributor><![CDATA[Korbicz, Józef (1951- ) - red.]]></dc:contributor>
<dc:contributor><![CDATA[Kowal, Marek - red.]]></dc:contributor>
<dc:date><![CDATA[2014]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/repozytorium/Content/78923/AMCS_2014_24_1_13.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/repozytorium/dlibra/publication/88736/edition/78923/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:78923]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[AMCS, volume 24, number 1 (2014)]]></dc:source>
<dc:source xml:lang="pl"><![CDATA[https://www.amcs.uz.zgora.pl/?action=papers&issue=56]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:88736]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
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