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03965nam a22005535i 4500 |
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978-3-540-49554-3 |
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DE-He213 |
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20151204184000.0 |
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100301s2007 gw | s |||| 0|eng d |
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|a 9783540495543
|9 978-3-540-49554-3
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|a 10.1007/978-3-540-49554-3
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|a 629.8
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|a Lin, Chong.
|e author.
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|a LMI Approach to Analysis and Control of Takagi-Sugeno Fuzzy Systems with Time Delay
|h [electronic resource] /
|c by Chong Lin, Qing-Guo Wang, Tong Heng Lee, Yong He.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg,
|c 2007.
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|a XI, 206 p.
|b online resource.
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|a text
|b txt
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|a computer
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|a online resource
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|a text file
|b PDF
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|a Lecture Notes in Control and Information Sciences,
|x 0170-8643 ;
|v 351
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|a Stability Analysis of T-S Fuzzy Systems -- Extension to Fuzzy Large-Scale Systems -- Stabilization Methods for T-S Fuzzy Systems -- Variable Structure Control for T-S Fuzzy Systems -- Observer-Based Fuzzy Control: Delay-Independent Method -- Observer-Based Fuzzy Control: Delay-Dependent Method -- Output Tracking Control for T-S Fuzzy Systems -- Fuzzy Filter Design for T-S Fuzzy Systems -- Descriptor Method for T-S Fuzzy Control Systems.
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|a SinceinitiatedbyLot?A. Zadehin1965,fuzzysettheoryhastriggeredacons- erably large body of areas to blossom. A fuzzy system is, in a very broad sense, anyfuzzylogic-basedsystemwherefuzzylogiccanbeusedeither asthebasisfor the representation of di?erent forms of system knowledge or the model for the interactions and relationships among the system variables. Fuzzy systems have proven to be an important tool for modeling complex systems for which, due to complexity or imprecision, classical tools are unsuccessful. There have been diverse ?elds of applications of fuzzy technology from medicine to management, from engineering to behavioral science, from vehicle control to computational linguistics, and so on. Fuzzy modeling is a conjunction to understand the s- tem’s behavior and build useful mathematical models. Di?erent types of fuzzy models have been proposed in the literature, among which the Takagi-Sugeno (T-S) fuzzy model is a rule-based one suitable for the accurate approximation and identi?cation of a wide class of nonlinear systems. There has been an - creasing amount of work on analysis and synthesis of fuzzy systems based on T-S fuzzy models. Since 2000, T-S fuzzy model approach has been extended to tackleanalysisandcontrolproblemsofnonlinear systemswith time delay. So far extensive results have been presented for investigating T-S fuzzy systems with time delay, many of which adopt an easy and popular scheme, say, linear matrix inequality (LMI) based method. However, there lacks of a monograph in this direction to provide the state-of-the-art of coverage of this new growing area.
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|a Engineering.
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|a Artificial intelligence.
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|a System theory.
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|a Control engineering.
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|a Robotics.
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|a Mechatronics.
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|a Engineering.
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|a Control.
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|a Artificial Intelligence (incl. Robotics).
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|a Control, Robotics, Mechatronics.
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|a Systems Theory, Control.
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| 700 |
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|a Wang, Qing-Guo.
|e author.
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|a Lee, Tong Heng.
|e author.
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|a He, Yong.
|e author.
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9783540495529
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| 830 |
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|a Lecture Notes in Control and Information Sciences,
|x 0170-8643 ;
|v 351
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|u http://dx.doi.org/10.1007/978-3-540-49554-3
|z Full Text via HEAL-Link
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|a ZDB-2-ENG
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| 950 |
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|a Engineering (Springer-11647)
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