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020			_a9783030731366
024	7		_a10.1007/978-3-030-73136-6 _2doi
040			_aTR-AnTOB _beng _erda _cTR-AnTOB
041			_aeng
050		4	_aTJ217
072		7	_aTBJ _2bicssc
072		7	_aGPFC _2bicssc
072		7	_aTEC009000 _2bisacsh
072		7	_aTBJ _2thema
072		7	_aGPFC _2thema
090			_aTJ217EBK
100	1		_aEsfandiari, Kasra. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aNeural Network-Based Adaptive Control of Uncertain Nonlinear Systems _h[electronic resource] / _cby Kasra Esfandiari, Farzaneh Abdollahi, Heidar A. Talebi.
250			_a1st ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aIntroduction -- Mathematical preliminaries -- NN-Based Adaptive Control of Affine Nonlinear Systems -- NN-Based Adaptive Control of Nonaffine Canonical Nonlinear -- Systems -- NN-Based Adaptive Control of Nonaffine Noncanonical Nonlinear -- NN-Based Adaptive Control of MIMO Nonaffine Noncanonical -- Nonlinear Systems.
520			_aThe focus of this book is the application of artificial neural networks in uncertain dynamical systems. It explains how to use neural networks in concert with adaptive techniques for system identification, state estimation, and control problems. The authors begin with a brief historical overview of adaptive control, followed by a review of mathematical preliminaries. In the subsequent chapters, they present several neural network-based control schemes. Each chapter starts with a concise introduction to the problem under study, and a neural network-based control strategy is designed for the simplest case scenario. After these designs are discussed, different practical limitations (i.e., saturation constraints and unavailability of all system states) are gradually added, and other control schemes are developed based on the primary scenario. Through these exercises, the authors present structures that not only provide mathematical tools for navigating control problems, but also supply solutions that are pertinent to real-life systems. Strengthens understanding of neural networks for readers working on control theory, including various mathematical proofs and analyses; Closely examines the use of neural networks for the control of uncertain dynamical systems; Facilitates implementation of adaptive structures using updating rules originating in optimization algorithms; Presents system identification, state estimation, and control schemes, applicable to a wide range of systems.
650		0	_aDynamics.
650		0	_aNonlinear theories.
650		0	_aArtificial intelligence.
650		0	_aNeural networks (Computer science) .
650		0	_aElectric power production.
650	1	4	_aApplied Dynamical Systems.
650	2	4	_aArtificial Intelligence.
650	2	4	_aMathematical Models of Cognitive Processes and Neural Networks.
650	2	4	_aElectrical Power Engineering.
653		0	_aAdaptive control systems
653		0	_aNeural networks (Computer science)
653		0	_aNonlinear systems -- Automatic control
700	1		_aAbdollahi, Farzaneh. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aTalebi, Heidar A. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
856	4	0	_uhttps://doi.org/10.1007/978-3-030-73136-6 _3Springer eBooks _zOnline access link to the resource
942			_2lcc _cEBK