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Titlebook: Identification and Stochastic Adaptive Control; Han-Fu Chen,Lei Guo Book 1991 Springer Science+Business Media New York 1991 Martingal.Mart

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书目名称Identification and Stochastic Adaptive Control
编辑Han-Fu Chen,Lei Guo
视频video
丛书名称Systems & Control: Foundations & Applications
图书封面Titlebook: Identification and Stochastic Adaptive Control;  Han-Fu Chen,Lei Guo Book 1991 Springer Science+Business Media New York 1991 Martingal.Mart
描述Identifying the input-output relationship of a system or discovering the evolutionary law of a signal on the basis of observation data, and applying the constructed mathematical model to predicting, controlling or extracting other useful information constitute a problem that has been drawing a lot of attention from engineering and gaining more and more importance in econo­ metrics, biology, environmental science and other related areas. Over the last 30-odd years, research on this problem has rapidly developed in various areas under different terms, such as time series analysis, signal processing and system identification. Since the randomness almost always exists in real systems and in observation data, and since the random process is sometimes used to model the uncertainty in systems, it is reasonable to consider the object as a stochastic system. In some applications identification can be carried out off line, but in other cases this is impossible, for example, when the structure or the parameter of the system depends on the sample, or when the system is time-varying. In these cases we have to identify the system on line and to adjust the control in accordance with the model whi
出版日期Book 1991
关键词Martingal; Martingale; Martingale difference sequence; Parameter; approximation; calculus; control; control
版次1
doihttps://doi.org/10.1007/978-1-4612-0429-9
isbn_softcover978-1-4612-6756-0
isbn_ebook978-1-4612-0429-9Series ISSN 2324-9749 Series E-ISSN 2324-9757
issn_series 2324-9749
copyrightSpringer Science+Business Media New York 1991
The information of publication is updating

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Coefficient Estimation for ARMAX Models,The system considered in this chapter is the same as that described by (3.91)−(3.94), for which it is assumed that (.) are the known upper bounds for system orders and . = 1 is the lower bound for time-delay. The assumption . = 1 is not a constraint, because in the case of . > 1 we may regard . as zero for 1 ≤ . < ..
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Stochastic Adaptive Tracking,We continue considering the system described by (3.91)−(3.94), for which (.) are assumed to be the known upper bounds for system orders and . = 1 to be the lower bound for the time-delay of the system. The true orders may be strictly less than . and . respectively. The system coefficients written in the matrix form.are unknown.
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Probability Theory Preliminaries,thout proof, but are explained by examples for those who are not familiar with measure-theory-based probability theory. For detailed material we refer readers to standard texts, for example, [Do], [Chu2], [Lo], [CT] and [Sh].
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