Constrained control machine learning (2 Ergebnisse)

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  • Sprache: Englisch

    Verlag: Springer Nature, 2025

    303202708X / 9783032027085

    • Hardcover

    Anbieter: Revaluation Books, Exeter, Vereinigtes KönigreichRevaluation Books

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    Zustand: Neu

    EUR 238,04

    EUR 14,58 Versand 
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    Anzahl: 1 verfügbar

    Hardcover. Zustand: Brand New. 150 pages. 9.26x6.11x9.49 inches. In Stock.

  • Sprache: Englisch

    Verlag: Springer, 2026

    303202708X / 9783032027085

    • Hardcover

    Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH

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    Zustand: Neu

    EUR 224,71

    EUR 30,50 Versand 
    Versand von Deutschland nach USA

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    Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book addresses the use of constrained control and machine learning approaches within data-driven settings in the field of autonomous robots for Industry 5.0 and Intelligent Transportation Systems. The primary aim of the book is to highlight the strict connection between constrained control and machine learning when tackling real-like phenomena in terms of a data-driven framework. The book shows how constrained control techniques and machine learning approaches can be adequately combined to derive novel and more efficient hybrid control architectures for data-driven based scenarios. To this end, several control problems ranging from planning and formation of autonomous multi-vehicles, routing decisions in urban road networks, freeway traffic modeling, to autonomous robotics in healthcare, are considered to highlight the capability of the data-driven approach to combine techniques coming from different research domains. The book is mainly devoted to researchers that, starting from a solid expertise on the constrained control and/or machine learning tools, would improve their ability to jointly use these technicalities in the data-driven setting.Addresses use of constrained control and machine learning within data-driven settings;Focuses on applications in autonomous robots for Industry 5.0 and intelligent transportation systems;Shows how combined constrained control and ML techniques can create efficient hybrid control architectures.