000			03926nam a22004695i 4500
001			u375400
003			SIRSI
005			20160812084322.0
007			cr nn 008mamaa
008			101125s2011 gw | s |||| 0|eng d
020			_a9783642167768 _9978-3-642-16776-8
040			_cMX-MeUAM
050		4	_aQ342
082	0	4	_a006.3 _223
100	1		_aLendek, Zsófia. _eauthor.
245	1	0	_aStability Analysis and Nonlinear Observer Design Using Takagi-Sugeno Fuzzy Models _h[recurso electrónico] / _cby Zsófia Lendek, Thierry Marie Guerra, Robert Babuška, Bart Schutter.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2011.
300			_aIX, 196 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStudies in Fuzziness and Soft Computing, _x1434-9922 ; _v262
505	0		_aIntroduction -- Takagi-Sugeno fuzzy models -- Stability analysis of TS fuzzy systems -- Observers for TS fuzzy systems -- Cascaded TS systems and observers -- Distributed TS systems and observers -- Adaptive observers for TS systems.
520			_aMany problems in decision making, monitoring, fault detection, and control require the knowledge of state variables and time-varying parameters that are not directly measured by sensors. In such situations, observers, or estimators, can be employed that use the measured input and output signals along with a dynamic model of the system in order to estimate the unknown states or parameters. An essential requirement in designing an observer is to guarantee the convergence of the estimates to the true values or at least to a small neighborhood around the true values. However, for nonlinear, large-scale, or time-varying systems, the design and tuning of an observer is generally complicated and involves large computational costs. This book provides a range of methods and tools to design observers for nonlinear systems represented by a special type of a dynamic nonlinear model - the Takagi-Sugeno (TS) fuzzy model. The TS model is a convex combination of affine linear models, which facilitates its stability analysis and observer design by using effective algorithms based on Lyapunov functions and linear matrix inequalities. Takagi-Sugeno models are known to be universal approximators and, in addition, a broad class of nonlinear systems can be exactly represented as a TS system. Three particular structures of large-scale TS models are considered: cascaded systems, distributed systems, and systems affected by unknown disturbances. The reader will find in-depth theoretic analysis accompanied by illustrative examples and simulations of real-world systems. Stability analysis of TS fuzzy systems is addressed in detail. The intended audience are graduate students and researchers both from academia and industry. For newcomers to the field, the book provides a concise introduction dynamic TS fuzzy models along with two methods to construct TS models for a given nonlinear system. For additional information, see the book website at http://www.dcsc.tudelft.nl/fuzzybook/
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650	1	4	_aEngineering.
650	2	4	_aComputational Intelligence.
650	2	4	_aArtificial Intelligence (incl. Robotics).
700	1		_aGuerra, Thierry Marie. _eauthor.
700	1		_aBabuška, Robert. _eauthor.
700	1		_aSchutter, Bart. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642167751
830		0	_aStudies in Fuzziness and Soft Computing, _x1434-9922 ; _v262
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-16776-8
596			_a19
942			_cLIBRO_ELEC
999			_c203280 _d203280