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008			200222s2020 gw | s |||| 0|eng d
020			_a9783030340940
024	7		_a10.1007/978-3-030-34094-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA402 _b2020 EB
245	0	0	_aOptimization, Learning, and Control for Interdependent Complex Networks _cedited by M. Hadi Amini
250			_aFirst edition 2020
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (X, 304 páginas) _b90 ilustraciones, 67 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aArchivo de texto _bPDF
490	0		_aAdvances in Intelligent Systems and Computing _x2194-5357 _v1123
490	0		_aEngineering (Springer-11647)
505	0		_aIntroduction -- Interdependent Complex Networks: Tale of IoT-based Smart Cities -- Deep Learning Algorithms for Energy Systems -- Distributed Algorithms for Interdependent Networks -- Online Optimization Learning for Interdependent Complex Networks -- Deep Learning Algorithms for Ramp Rate Prediction in Unit Commitment -- Networked Control Systems: Case Study of Unmanned Aerial Vehicle -- Conclusion.
520	3		_aThis 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.
988			_aSpringer_Engineering_31032020
650		7	_2embne _aAnálisis de sistemas _9138446
650		7	_2embne _9145705 _aOptimización matemática
700	1		_aAmini, M. Hadi _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783030340933
776	0	8	_iPrinted edition: _z9783030340957
776	0	8	_iPrinted edition: _z9783030340964
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-34094-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2020 _dz _ek _zSI