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Ajouter au panierPaperback. Etat : Brand New. 316 pages. 9.25x6.10x0.75 inches. In Stock.
Edité par Springer International Publishing, Springer International Publishing Feb 2021, 2021
ISBN 10 : 3030340961 ISBN 13 : 9783030340964
Langue: anglais
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Ajouter au panierTaschenbuch. Etat : Neu. Neuware -This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role in smart cities operation, smart energy systems, and intelligent transportation networks. It paves the way for researchers working on optimization, learning, and control spread over the ¿elds of computer science, operation research, electrical engineering, civil engineering, and system engineering. This book also covers optimization algorithms for large-scale problems from theoretical foundations to real-world applications, learning-based methods to enable intelligence in smart cities, and control techniques to deal with the optimal and robust operation of complex systems. It further introduces novel algorithms for data analytics in large-scale interdependent complex networks. ¿ Speci¿es the importance of efficient theoretical optimization and learning methods in dealing with emerging problems in the context of interdependent networks¿ Provides a comprehensive investigation of advance data analytics and machine learning algorithms for large-scale complex networks¿ Presents basics and mathematical foundations needed to enable efficient decision making and intelligence in interdependent complex networksM. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 316 pp. Englisch.
Edité par Springer International Publishing, 2021
ISBN 10 : 3030340961 ISBN 13 : 9783030340964
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Ajouter au panierTaschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role in smart cities operation, smart energy systems, and intelligent transportation networks. It paves the way for researchers working on optimization, learning, and control spread over the fields of computer science, operation research, electrical engineering, civil engineering, and system engineering. This book also covers optimization algorithms for large-scale problems from theoretical foundations to real-world applications, learning-based methods to enable intelligence in smart cities, and control techniques to deal with the optimal and robust operation of complex systems. It further introduces novel algorithms for data analytics in large-scale interdependent complex networks. - Specifies the importance of efficient theoretical optimization and learning methods in dealing with emerging problems in the context of interdependent networks - Provides a comprehensive investigation of advance data analytics and machine learning algorithms for large-scale complex networks - Presents basics and mathematical foundations needed to enable efficient decision making and intelligence in interdependent complex networks M. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011.
Edité par Springer International Publishing, 2021
ISBN 10 : 3030340961 ISBN 13 : 9783030340964
Langue: anglais
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Ajouter au panierTaschenbuch. Etat : Neu. Optimization, Learning, and Control for Interdependent Complex Networks | M. Hadi Amini | Taschenbuch | x | Englisch | 2021 | Springer International Publishing | EAN 9783030340964 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Edité par Springer, Berlin, Springer International Publishing, Springer, 2020
ISBN 10 : 3030340937 ISBN 13 : 9783030340933
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Ajouter au panierBuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role in smart cities operation, smart energy systems, and intelligent transportation networks. It paves the way for researchers working on optimization, learning, and control spread over the fields of computer science, operation research, electrical engineering, civil engineering, and system engineering. This book also covers optimization algorithms for large-scale problems from theoretical foundations to real-world applications, learning-based methods to enable intelligence in smart cities, and control techniques to deal with the optimal and robust operation of complex systems. It further introduces novel algorithms for data analytics in large-scale interdependent complex networks. - Specifies the importance of efficient theoretical optimization and learning methods in dealing with emerging problems in the context of interdependent networks - Provides a comprehensive investigation of advance data analytics and machine learning algorithms for large-scale complex networks - Presents basics and mathematical foundations needed to enable efficient decision making and intelligence in interdependent complex networks M. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011.
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Ajouter au panierPaperback. Etat : New. New. book.
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Ajouter au panierHardcover. Etat : New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Edité par Springer International Publishing Feb 2021, 2021
ISBN 10 : 3030340961 ISBN 13 : 9783030340964
Langue: anglais
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 53,49
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Ajouter au panierTaschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role in smart cities operation, smart energy systems, and intelligent transportation networks. It paves the way for researchers working on optimization, learning, and control spread over the fields of computer science, operation research, electrical engineering, civil engineering, and system engineering. This book also covers optimization algorithms for large-scale problems from theoretical foundations to real-world applications, learning-based methods to enable intelligence in smart cities, and control techniques to deal with the optimal and robust operation of complex systems. It further introduces novel algorithms for data analytics in large-scale interdependent complex networks. - Specifies the importance of efficient theoretical optimization and learning methods in dealing with emerging problems in the context of interdependent networks - Provides a comprehensive investigation of advance data analytics and machine learning algorithms for large-scale complex networks - Presents basics and mathematical foundations needed to enable efficient decision making and intelligence in interdependent complex networks M. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011. 316 pp. Englisch.
Edité par Berlin Springer International Publishing Springer Feb 2020, 2020
ISBN 10 : 3030340937 ISBN 13 : 9783030340933
Langue: anglais
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Ajouter au panierBuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role in smart cities operation, smart energy systems, and intelligent transportation networks. It paves the way for researchers working on optimization, learning, and control spread over the fields of computer science, operation research, electrical engineering, civil engineering, and system engineering. This book also covers optimization algorithms for large-scale problems from theoretical foundations to real-world applications, learning-based methods to enable intelligence in smart cities, and control techniques to deal with the optimal and robust operation of complex systems. It further introduces novel algorithms for data analytics in large-scale interdependent complex networks. - Specifies the importance of efficient theoretical optimization and learning methods in dealing with emerging problems in the context of interdependent networks - Provides a comprehensive investigation of advance data analytics and machine learning algorithms for large-scale complex networks - Presents basics and mathematical foundations needed to enable efficient decision making and intelligence in interdependent complex networks M. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011. 304 pp. Englisch.
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Edité par Springer International Publishing, 2021
ISBN 10 : 3030340961 ISBN 13 : 9783030340964
Langue: anglais
Vendeur : moluna, Greven, Allemagne
EUR 48,37
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Ajouter au panierKartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Specifies the importance of efficient theoretical methods in dealing with problems in the context of interdependent networksProvides a comprehensive investigation of recently developed algorithms for largescale networksPresents basics and m.
Edité par Springer International Publishing, 2020
ISBN 10 : 3030340937 ISBN 13 : 9783030340933
Langue: anglais
Vendeur : moluna, Greven, Allemagne
EUR 48,74
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Ajouter au panierGebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Specifies the importance of efficient theoretical methods in dealing with problems in the context of interdependent networksProvides a comprehensive investigation of recently developed algorithms for largescale networksPresents basics and m.