000			03834nam a22005055i 4500
001			978-1-4471-4914-9
003			DE-He213
005			20140220082808.0
007			cr nn 008mamaa
008			130107s2013 xxk| s |||| 0|eng d
020			_a9781447149149 _9978-1-4471-4914-9
024	7		_a10.1007/978-1-4471-4914-9 _2doi
050		4	_aTJ212-225
072		7	_aTJFM _2bicssc
072		7	_aTEC004000 _2bisacsh
082	0	4	_a629.8 _223
100	1		_aShen, Bo. _eauthor.
245	1	0	_aNonlinear Stochastic Systems with Incomplete Information _h[electronic resource] : _bFiltering and Control / _cby Bo Shen, Zidong Wang, Huisheng Shu.
264		1	_aLondon : _bSpringer London : _bImprint: Springer, _c2013.
300			_aXVI, 248 p. 68 illus., 31 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aFrom the Contents: Quantized H-infinity Control for Nonlinear Stochastic Time-delay Systems with Missing Measurements -- Nonlinear H-infinity Filtering for Discrete-Time Stochastic Systems with Missing Measurements and Randomly Varying Sensor Delays -- Robust H-infinity Filtering with Randomly Occurring Nonlinearities, Quantization Effects and Successive Packet Dropouts -- H-infinity Filtering with Randomly Occurring Sensor Saturations and Missing Measurements -- Distributed H-infinity Consensus Filtering in Sensor Networks with Multiple Missing Measurements: The Finite-Horizon Case.
520			_aNonlinear Stochastic Processes addresses the frequently-encountered problem of incomplete information. The causes of this problem considered here include: missing measurements; sensor delays and saturation; quantization effects; and signal sampling. Divided into three parts, the text begins with a focus on H∞ filtering and control problems associated with general classes of nonlinear stochastic discrete-time systems. Filtering problems are considered in the second part, and in the third the theory and techniques previously developed are applied to the solution of issues arising in complex networks with the design of sampled-data-based controllers and filters. Among its highlights, the text provides: ·         a unified framework for handling filtering and control problems in complex communication networks with limited bandwidth; ·         new concepts such as random sensor and signal saturations for more realistic modeling; and ·         demonstration of the use of techniques such as the Hamilton–Jacobi–Isaacs, difference linear matrix, and parameter-dependent matrix inequalities and sums of squares to handle the computational challenges inherent in these systems. The collection of recent research results presented in Nonlinear Stochastic Processes will be of interest to academic researchers in control and signal processing. Graduate students working with communication networks with lossy information and control of stochastic systems will also benefit from reading the book.
650		0	_aEngineering.
650		0	_aSystems theory.
650		0	_aDistribution (Probability theory).
650		0	_aTelecommunication.
650	1	4	_aEngineering.
650	2	4	_aControl.
650	2	4	_aProbability Theory and Stochastic Processes.
650	2	4	_aCommunications Engineering, Networks.
650	2	4	_aSignal, Image and Speech Processing.
650	2	4	_aSystems Theory, Control.
700	1		_aWang, Zidong. _eauthor.
700	1		_aShu, Huisheng. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781447149132
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4471-4914-9
912			_aZDB-2-ENG
999			_c94734 _d94734