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Titlebook: Recent Advances in Systems, Control and Information Technology; Proceedings of the I Roman Szewczyk,Ma?gorzata Kaliczyńska Conference proce

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書(shū)目名稱Recent Advances in Systems, Control and Information Technology
副標(biāo)題Proceedings of the I
編輯Roman Szewczyk,Ma?gorzata Kaliczyńska
視頻videohttp://file.papertrans.cn/824/823016/823016.mp4
概述Presents recent advances in systems, control, and information technology.Proceedings of the International Conference Systems, Control and Information Technology SCIT 2016 held on May 20–21 in Warsaw,
叢書(shū)名稱Advances in Intelligent Systems and Computing
圖書(shū)封面Titlebook: Recent Advances in Systems, Control and Information Technology; Proceedings of the I Roman Szewczyk,Ma?gorzata Kaliczyńska Conference proce
描述.This book presents the proceedings of the International Conference on Systems, Control and Information Technologies 2016. It includes research findings from leading experts in the fields connected with INDUSTRY 4.0 and its implementation, especially: intelligent systems, advanced control, information technologies, industrial automation, robotics, intelligent sensors, metrology and new materials. Each chapter offers an analysis of a specific technical problem followed by a numerical analysis and simulation as well as the implementation for the solution of a real-world problem..
出版日期Conference proceedings 2017
關(guān)鍵詞Intelligent Systems; Systems; Control; Information Technology; SCIT2016; SCIT
版次1
doihttps://doi.org/10.1007/978-3-319-48923-0
isbn_softcover978-3-319-48922-3
isbn_ebook978-3-319-48923-0Series ISSN 2194-5357 Series E-ISSN 2194-5365
issn_series 2194-5357
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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Advances in Intelligent Systems and Computinghttp://image.papertrans.cn/r/image/823016.jpg
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https://doi.org/10.1007/978-3-319-48923-0Intelligent Systems; Systems; Control; Information Technology; SCIT2016; SCIT
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Parallel Computer System Resource Planning for Synthesis of Neuro-Fuzzy Networkshod, the parameters of solved applied problem (the number of instances and the number of features in a given training sample). The synthesized model enables to estimate the time required parallel system to perform the method of synthesis of neuro-fuzzy networks.
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