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Titlebook: Stochastic Switching Systems; Analysis and Design El-Kébir Boukas Book 2006 Birkh?user Boston 2006 Analysis.Markov.communication.filtering

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發(fā)表于 2025-3-21 16:55:12 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Stochastic Switching Systems
副標題Analysis and Design
編輯El-Kébir Boukas
視頻videohttp://file.papertrans.cn/879/878170/878170.mp4
概述Up-to-date methods and techniques for the analysis and design of various control systems with or without uncertainties.Included are many numerical examples as well as LMI analysis methods and design a
叢書名稱Control Engineering
圖書封面Titlebook: Stochastic Switching Systems; Analysis and Design El-Kébir Boukas Book 2006 Birkh?user Boston 2006 Analysis.Markov.communication.filtering
描述.Stochastic switching systems represent an interesting class of systems that can be used to model a variety of systems having abrupt random changes in their dynamics. Such systems may be found in the fields of manufacturing, communications, aerospace, power, and economics...This work presents stochastic switching systems and provides up-to-date methods and techniques for the analysis and design of various control systems with or without uncertainties. An introductory chapter highlights basic concepts and practical models, which are then used to solve more advanced problems throughout the book. Included are many numerical examples as well as LMI analysis methods and design approaches to supplement the developed results...Stochastic Switching Systems. may be used as a supplementary textbook for graduate-level engineering courses, or as a reference for control engineers, graduate students, and researchers in systems and control. Prerequisites include elementary courses in matrix theory, probability, optimization techniques, and control systems theory..
出版日期Book 2006
關鍵詞Analysis; Markov; communication; filtering problem; matrix theory; model; optimization; stability; systems t
版次1
doihttps://doi.org/10.1007/0-8176-4452-0
isbn_ebook978-0-8176-4452-9Series ISSN 2373-7719 Series E-ISSN 2373-7727
issn_series 2373-7719
copyrightBirkh?user Boston 2006
The information of publication is updating

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resentative of simple adaptive control parameter adjusting strategy for DE. Repeated simulations for two different combinations of driving chaotic systems were performed on the IEEE CEC 13 benchmark set. Finally, the obtained results are compared with the canonical not-chaotic jDE.
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to the capability of achieving absolute accuracy with less number of features. Experimental results showed the superiority of ILP-PRKELM with an accomplished accuracy of 84.6 and 78.6 for Last.fm and Douban respectively, which is equivalent to 2% improved accuracy over the benchmarks.
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en subject analyses, with accuracy getting worse with the increase of EEG segments. The contribution of this work is two-fold: the practical application allows for a reliable adoption of machine learning in non-invasive EEG screening of small ADHD dataset, while the theoretical contribution extends
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982), Irwin and Yang (1982), Chao and Friesz (1984), Daffermos and Nagurney (1984), and Tobin (1984, 1987); computational evaluations by Meister, Chen and Heady (1978), and Nagurney (1987b); iterative solution methods by Pang and Chan (1982), and Irwin and Yang (1982); a linear complementarity formu
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