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020			_a9783031478352
024	7		_a10.1007/978-3-031-47835-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.87 _b2024 EB
100	1		_aWang, Junyi _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9690415
245	1	0	_aSynchronization Control of Markovian Complex Neural Networks with Time-varying Delays _cby Junyi Wang, Jun Fu
250			_a1st ed. 2024
264		1	_aCham _bSpringer International Publishing _c2024
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aStudies in Systems, Decision and Control _x2198-4190 _v514
505	0		_aIntroduction -- Stochastic synchronization of Markovian coupled neural networks with partially unknown transition rates -- Local synchronization of Markovian neutral-type complex networks with partially unknown transition rates -- Local Synchronization of Markovian nonlinearly coupled neural networks with generally uncertain transition rates -- Sampled-data synchronization of complex networks based on discontinuous Lyapunov-Krasovskii functional -- Sampled-data synchronization of Markovian coupled neural networks with time-varying mode delays -- Synchronization criteria of delayed inertial neural networks with generally uncertain transition rates -- Conclusions.
520			_aThis monograph studies the synchronization control of Markovian complex neural networks with time-varying delays, and the structure of the book is summarized as follows. Chapter 1 introduces the system description and some background knowledges, and also addresses the motivations of this monograph. In Chapter 2, the stochastic synchronization issue of Markovian coupled neural networks with partially unknown transition rates and random coupling strengths is investigated. In Chapter 3, the local synchronization issue of Markovian neutral complex networks with partially information of transition rates is investigated. The new delay-dependent synchronization criteria in terms of LMIs are derived, which depends on the upper and lower bounds of the delays. In Chapter 4, the local synchronization issue of Markovian nonlinear coupled neural networks with uncertain and partially unknown transition rates is investigated. The less conservative local synchronization criteria containing the bounds of delay and delay derivative are obtained based on the novel augmented Lyapunov-Krasovskii functional and a new integral inequality. In Chapter 5, the sampled-data synchronization issue of delayed complex networks with aperiodic sampling interval is investigated based on enhanced input delay approach, which makes full use of the upper bound of the variable sampling interval and the sawtooth structure information of varying input delay. In Chapter 6, the sampled-data synchronization issue of Markovian coupled neural networks with mode-dependent interval time-varying delays and aperiodic sampling intervals is investigated based on an enhanced input delay approach. Furthermore, the mode-dependent sampled-data controllers are proposed based on the delay dependent synchronization criteria. In Chapter 7, the synchronization issue of inertial neural networks with time-varying delays and generally Markovian jumping is investigated. In Chapter 8, we conclude the monograph by briefly summarizing the main theoretical findings.
988			_aSpringer_Engineering_2024
650		7	_2embne _9678664 _aRedes neuronales artificiales
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-47835-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b06/2024 _dz _eIG _zSI