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  • This article deals with Analytic Programming (AP) which was proven to be highly effective tool of Artificial Neural Network (ANN) synthesis and optimization. AP is successfully applied here to evolve such ANN which is able to approximate heating power consumption of an agglomeration, in dependence on time and atmospherics temperature, more accurately than standard methods. An experiment described in the paper was realized on real life data from Most agglomeration and heating plant situated in Komořany, Czech Republic. Resulted ANN brings 19% increase of approximation accuracy.
  • This article deals with Analytic Programming (AP) which was proven to be highly effective tool of Artificial Neural Network (ANN) synthesis and optimization. AP is successfully applied here to evolve such ANN which is able to approximate heating power consumption of an agglomeration, in dependence on time and atmospherics temperature, more accurately than standard methods. An experiment described in the paper was realized on real life data from Most agglomeration and heating plant situated in Komořany, Czech Republic. Resulted ANN brings 19% increase of approximation accuracy. (en)
Title
  • ANN Synthesis for an Agglomeration Heating Power Consumption Approximation
  • ANN Synthesis for an Agglomeration Heating Power Consumption Approximation (en)
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  • ANN Synthesis for an Agglomeration Heating Power Consumption Approximation
  • ANN Synthesis for an Agglomeration Heating Power Consumption Approximation (en)
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  • RIV/70883521:28140/11:43867243!RIV12-MSM-28140___
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  • 186317
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  • RIV/70883521:28140/11:43867243
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  • Neural Network, approximation, heating plant, Analytic Programming, SOMA, optimization (en)
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  • Jašek, Roman
  • Vařacha, Pavel
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  • WSEAS Press
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  • 978-1-61804-004-6
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  • 28140
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