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  • This paper presents a new technique for process identication. Since once nonlinear problem is described by piecewise-linear structure, it may be solved by many well-known techniques, the result of introduced technique provides a set of linear equations, where each of these equations is valid in some region of state space and together, they approximate whole nonlinear process. In the first five paragraphs, the technique is theoretically described and the paper is finished with demonstrative example.
  • This paper presents a new technique for process identication. Since once nonlinear problem is described by piecewise-linear structure, it may be solved by many well-known techniques, the result of introduced technique provides a set of linear equations, where each of these equations is valid in some region of state space and together, they approximate whole nonlinear process. In the first five paragraphs, the technique is theoretically described and the paper is finished with demonstrative example. (en)
Title
  • Possibility of Dynamical Process Linearization Using Piecewise-Linear Neural Network
  • Possibility of Dynamical Process Linearization Using Piecewise-Linear Neural Network (en)
skos:prefLabel
  • Possibility of Dynamical Process Linearization Using Piecewise-Linear Neural Network
  • Possibility of Dynamical Process Linearization Using Piecewise-Linear Neural Network (en)
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  • RIV/00216275:25530/14:39898586!RIV15-MSM-25530___
http://linked.open...avai/riv/aktivita
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  • S
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  • 37900
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  • RIV/00216275:25530/14:39898586
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  • Modelling; Linearization; Neural networks (en)
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  • [41023B514AF2]
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  • Ostrava
http://linked.open...i/riv/mistoVydani
  • Heidelberg
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  • Nostradamus 2014: Prediction, Modeling and Analysis of Complex Systems
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  • Doležel, Petr
  • Mariška, Martin
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issn
  • 2194-5357
number of pages
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  • 10.1007/978-3-319-07401-6_22
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  • Springer-Verlag
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  • 978-3-319-07400-9
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  • 25530
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