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020			_a9783030296933
024	7		_a10.1007/978-3-030-29693-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
041	0		_aeng
050		4	_aQA402.3 _b2020 EB
100	1		_aZoppoli, Riccardo _eautor _9672564
245	0	0	_aNeural Approximations for Optimal Control and Decision _cby Riccardo Zoppoli, Marcello Sanguineti, Giorgio Gnecco, Thomas Parisini.
250			_aFirst edition
264		1	_aCham _bSpringer International Publishing : _bImprint Springer _c2020
300			_a1 recurso en línea (XVIII, 517 páginas) _b99 ilustraciones, 8 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aCommunications and Control Engineering _x0178-5354
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aChapter 1. The Basic Infinite-Dimensional or Functional Optimization Problem -- Chapter 2. From Functional Optimization to Nonlinear Programming by the Extended Ritz Method -- Chapter 3. Some Families of FSP Functions and Their Properties -- Chapter 4. Design of Mathematical Models by Learning from Data and FSP Functions -- Chapter 5. Numerical Methods for Integration and Search for Minima -- Chapter 6. Deterministic Optimal Control Over a Finite Horizon -- Chapter 7. Stochastic Optimal Control with Perfect State Information over a Finite Horizon -- Chapter 8. Stochastic Optimal Control with Imperfect State Information over a Finite Horizon -- Chapter 9. Team Optimal Control Problems -- Chapter 10. Optimal Control Problems over an Infinite Horizon -- Index.
520	3		_aNeural Approximations for Optimal Control and Decision provides a comprehensive methodology for the approximate solution of functional optimization problems using neural networks and other nonlinear approximators where the use of traditional optimal control tools is prohibited by complicating factors like non-Gaussian noise, strong nonlinearities, large dimension of state and control vectors, etc. Features of the text include: • a general functional optimization framework; • thorough illustration of recent theoretical insights into the approximate solutions of complex functional optimization problems; • comparison of classical and neural-network based methods of approximate solution; • bounds to the errors of approximate solutions; • solution algorithms for optimal control and decision in deterministic or stochastic environments with perfect or imperfect state measurements over a finite or infinite time horizon and with one decision maker or several; • applications of current interest: routing in communications networks, traffic control, water resource management, etc.; and • numerous, numerically detailed examples. The authors' diverse backgrounds in systems and control theory, approximation theory, machine learning, and operations research lend the book a range of expertise and subject matter appealing to academics and graduate students in any of those disciplines together with computer science and other areas of engineering.
988			_aPrimersemestre_2020_Robotics
650		7	_2embne _9145606 _aControl, Teoría de
650		7	_2embne _aSistemas, Teoría de _9141328
700	1		_aSanguineti, Marcello _eautor _0(orcid)0000-0003-0355-8483 _1https://orcid.org/0000-0003-0355-8483 _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aGnecco, Giorgio _eautor _0(orcid)0000-0002-5427-4328 _1https://orcid.org/0000-0002-5427-4328 _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aParisini, Thomas _eautor _0(orcid)0000-0001-5396-9665 _1https://orcid.org/0000-0001-5396-9665 _4aut _4http://id.loc.gov/vocabulary/relators/aut
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783030296919
776	0	8	_iPrinted edition: _z9783030296926
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-29693-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2020 _dz _eb _zSI