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  • The aim of this paper is to design and train artificial neural networks for classification analysis of samples that have passed mechanical tests. Samples of materials are Relanex, Relastic, Lamplex and paperboard. Based on the mechanical tests results (temperature, exposure time, bending resistance and impact resistance) neural networks can identify the type of material with great precision. All samples are used for networks training, validation process is based on medians of groups. For this purpose are used Multilayer perceptron networks (MLPN) and RBF networks.
  • The aim of this paper is to design and train artificial neural networks for classification analysis of samples that have passed mechanical tests. Samples of materials are Relanex, Relastic, Lamplex and paperboard. Based on the mechanical tests results (temperature, exposure time, bending resistance and impact resistance) neural networks can identify the type of material with great precision. All samples are used for networks training, validation process is based on medians of groups. For this purpose are used Multilayer perceptron networks (MLPN) and RBF networks. (en)
Title
  • The use of artificial neural networks in SW STATISTICA for classification analysis of samples that have passed mechanical tests
  • The use of artificial neural networks in SW STATISTICA for classification analysis of samples that have passed mechanical tests (en)
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  • The use of artificial neural networks in SW STATISTICA for classification analysis of samples that have passed mechanical tests
  • The use of artificial neural networks in SW STATISTICA for classification analysis of samples that have passed mechanical tests (en)
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  • RIV/49777513:23220/13:43918989!RIV14-MSM-23220___
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  • I
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  • 112937
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  • RIV/49777513:23220/13:43918989
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  • RBF networks; multilayer perceptron networks; mechanical tests; classification analysis; artificial neural networks (en)
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  • [68A5ECF57921]
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  • Plzeň
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  • Pilsen
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  • Diagnostika '13
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  • Kupka, Lukáš
  • Tůmová, Olga
  • Langhammer, Tomáš
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  • Západočeská univerzita v Plzni
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  • 978-80-261-0210-6
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  • 23220
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