000			03603nam a22004215i 4500
650		7	_2embne _aRedes neuronales artificiales _9678664
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020			_a9789813364745
024	7		_a10.1007/978-981-33-6474-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _erda _dES-MaUEC
050		4	_aQA76.87 _b2021 EB
100	1		_aHe, Shuping _eautor _0(orcid)0000-0003-1869-2116 _1https://orcid.org/0000-0003-1869-2116 _4aut _4http://id.loc.gov/vocabulary/relators/aut _9677878
245	1	0	_aMulti-model Jumping Systems: Robust Filtering and Fault Detection _cby Shuping He, Xiaoli Luan.
250			_aFirst edition 2021
264		1	_aSingapore _bSpringer International Pulishing _c2021
300			_a1 recurso en línea (XIII, 182 páginas) _b45 ilustraciones, 8 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2
490	0		_aIntelligent Technologies and Robotics (SpringerNature-42732)
490	0		_aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505	0		_aIntroduction -- Robust Filtering -- Robust filtering for jumping systems -- Finite-time robust filtering for jumping systems -- Finite-frequency robust filtering for jumping systems -- Higher order moment robust filtering for jumping systems -- Fault Detection -- Robust fault detection for jumping systems -- Observer-based robust fault detection for fuzzy jumping systems -- Filtering-based robust fault detection of fuzzy jumping systems -- Neural network-based robust fault detection for nonlinear jumping systems -- Conclusion.
520	3		_aThis book focuses on multi-model systems, describing how to apply intelligent technologies to model complex multi-model systems by combining stochastic jumping system, neural network and fuzzy models. It focuses on robust filtering, including finite-time robust filtering, finite-frequency robust filtering and higher order moment robust filtering schemes, as well as fault detection problems for multi-model jump systems, such as observer-based robust fault detection, filtering-based robust fault detection and neural network-based robust fault detection methods. The book also demonstrates the validity and practicability of the theoretical results using simulation and practical examples, like circuit systems, robot systems and power systems. Further, it introduces readers to methods such as finite-time filtering, finite-frequency robust filtering, as well as higher order moment and neural network-based fault detection methods for multi-model jumping systems, allowing them to grasp the modeling, analysis and design of the multi-model systems presented and implement filtering and fault detection analysis for various systems, including circuit, network and mechanical systems.
988			_aSpringer_Robotics_2021
700	1		_aLuan, Xiaoli _eautor _0(orcid)0000-0002-4805-1726 _1https://orcid.org/0000-0002-4805-1726 _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9789813364738
776	0	8	_iPrinted edition: _z9789813364752
776	0	8	_iPrinted edition: _z9789813364769
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-33-6474-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2021 _dz _eIG _zSI