000			03117nam a2200349Ia 4500
001			CRC00E75586PDF
003			FlBoTFG
005			20230510163239.0
006			m o d
007			cr cn
008			111202s2012 fluad ob 001 0 eng d
020			_a9780429139871
035			_a(OCoLC)762324952
040			_aFlBoTFG _cFlBoTFG
082			_a658.50727 _bB136
090			_aTS156.8 _b.B343 2012
100	1		_aBadiru, Adedeji B., Ibidapo-Obe, Oye and Ayeni, Babatunde J.
245	1	0	_aIndustrial Control Systems : _hMathematical and Statistical Models and Techniques
260			_aBoca Raton : _bCRC Press, _cc2012.
300			_a382p.
490	1		_aIndustrial innovation series
505	0		_ach. 1. Mathematical modeling for product design -- ch. 2. Dynamic fuzzy systems modeling -- ch. 3. Stochastic systems modeling -- ch. 4. Systems optimization techniques -- ch. 5. Statistical control techniques -- ch. 6. Design of experiment techniques -- ch. 7. Risk analysis and estimation techniques -- ch. 8. Mathematical modeling and control of multi-constrained projects -- ch. 9. Online support vector regression with varying parameters for time-dependent data.
520			_aPreface This book presents the mathematical foundation for building and implementing industrial control systems. It contains mathematically rigorous models and techniques for control systems, in general, with specific orientation toward industrial systems. Industrial control encompasses several types of control systems. Some common elements of industrial control systems include supervisory control and data acquisition systems, distributed control systems, and other generic control system configurations, such as programmable logic controllers, that are often found in industrial operations and engineering infrastructure. Industrial control systems are not limited to production or manufacturing enterprises, as they are typically used in general industries such as electrical, water, oil and gas, and data acquisition devices. Based on information received from remote sensors, automated commands can be sent to remote control devices, which are referred to as field devices. Field devices are used to control local operations. These may include opening and closing valves, tripping breakers, collecting data from sensors, and monitoring local operating conditions. All of these are governed by some form of mathematical representation. Thus, this book has great importance in linking theory and practice. Distributed control systems are used to control industrial processes such as electric power generation, oil and gas refineries, water and wastewater treatment, and chemical, food, and automotive production. -- _cProvided by publisher.
538			_aMode of access: World Wide Web.
650		0	_aProcess control _xStatistical methods.
650		0	_aProcess control _xMathematical models.
655		7	_aElectronic books. _2lcsh
776	1		_z9781420075588 (hardback)
830		0	_aIndustrial innovation series.
856	4	0	_uhttp://marc.crcnetbase.com/isbn/9781420075595 _qebook
942			_2ddc _cEBK
999			_c1002 _d1002