000			03954cam a2200481Ii 4500
001			on1107041940
003			OCoLC
005			20220711203524.0
006			m o d
007			cr cnu---unuuu
008			190702s2019 nju o 000 0 eng d
040			_aN$T _beng _erda _epn _cN$T _dN$T _dOCLCF _dYDX _dRECBK _dDG1 _dUIU
020			_a9781119491545 _q(electronic bk.)
020			_a1119491541 _q(electronic bk.)
020			_a9781119491514 _q(electronic bk. : oBook)
020			_a1119491517 _q(electronic bk. : oBook)
020			_z9781119491552
020			_z111949155X
029	1		_aAU@ _b000066121160
035			_a(OCoLC)1107041940
050		4	_aTA654.9
082	0	4	_a624.1 _223
049			_aMAIN
100	1		_aYu, Wen, _cprofesor titular, _eauthor. _98458
245	1	0	_aModeling and control of uncertain nonlinear systems with fuzzy equations and Z-number / _cWen Yu, Raheleh Jafari.
264		1	_aHoboken, New Jersey : _bWiley, _c2019.
300			_a1 online resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aIEEE Press series on systems science and engineering
588	0		_aOnline resource; title from PDF title page (EBSCO, viewed July 8, 2019)
520			_aAn original, systematic-solution approach to uncertain nonlinear systems control and modeling using fuzzy equations and fuzzy differential equations There are various numerical and analytical approaches to the modeling and control of uncertain nonlinear systems. Fuzzy logic theory is an increasingly popular method used to solve inconvenience problems in nonlinear modeling. Modeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and Z-Number presents a structured approach to the control and modeling of uncertain nonlinear systems in industry using fuzzy equations and fuzzy differential equations. The first major work to explore methods based on neural networks and Bernstein neural networks, this innovative volume provides a framework for control and modeling of uncertain nonlinear systems with applications to industry. Readers learn how to use fuzzy techniques to solve scientific and engineering problems and understand intelligent control design and applications. The text assembles the results of four years of research on control of uncertain nonlinear systems with dual fuzzy equations, fuzzy modeling for uncertain nonlinear systems with fuzzy equations, the numerical solution of fuzzy equations with Z-numbers, and the numerical solution of fuzzy differential equations with Z-numbers. Using clear and accessible language to explain concepts and principles applicable to real-world scenarios, this book: -Presents the modeling and control of uncertain nonlinear systems with fuzzy equations and fuzzy differential equations -Includes an overview of uncertain nonlinear systems for non-specialists -Teaches readers to use simulation, modeling and verification skills valuable for scientific research and engineering systems development -Reinforces comprehension with illustrations, tables, examples, and simulations Modeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and Z-Number is suitable as a textbook for advanced students, academic and industrial researchers, and practitioners in fields of systems engineering, learning control systems, neural networks, computational intelligence, and fuzzy logic control.
650		0	_aStructural control (Engineering) _98459
650		7	_aTECHNOLOGY & ENGINEERING / Electronics / General. _2bisacsh _96758
650		7	_aStructural control (Engineering) _2fast _0(OCoLC)fst01135626 _98459
655		4	_aElectronic books. _93294
700	1		_aJafari, Raheleh, _eauthor. _98460
830		0	_aIEEE Press series on systems science and engineering. _98461
856	4	0	_uhttps://doi.org/10.1002/9781119491514 _zWiley Online Library
942			_cEBK
994			_a92 _bDG1
999			_c69101 _d69101