9780817635978 - Identification and Stochastic Adaptive Control (Systems & Control: Foundations & Applications) de Han-Fu Chen; Guo, Lei (18 resultados)

Hardcover. Condición: Very Good. Slight dent to bottom edge of front cover, otherwise text clean and tight; no dust jacket; Systems & Control: Foundations & Applications; 0.8 x 9.5 x 6.1 Inches; 452 pages.

Condición: New. pp. 452.

Condición: New. pp. 452 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.

Condición: New. pp. 452.

Hard Cover. Condición: Good. First Hardback Edition. 240 x 165mm. pp. 435. English text. First hardback edition of 'Identification and Stochastic Adaptive Control' by Han-Fu Chen and Lei Guo. Bound in original green and grey boards. No dust jacket, as published. Library marks to f.e.p's and base of spine. Also some fading to spine and light wave to pages. Binding strong. No underlining. Ex-Library.

Condición: New.

Condición: New.

Hardcover. Condición: new. Hardcover. 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 which is supposed to be approaching the true system during the process of identification. This is why there has been an increasing interest in identification and adaptive control for stochastic systems from both theorists and practitioners. The basic model that is used to describe systems in this book is ARMAX (or its generalizations) which is widely and successfully used in applications. The opening chapters present preliminaries such as the fundamentals of probability theory and both classical and refined results in martingale limit theory and some facts from linear systems. Subsequ Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condición: New. In.

Condición: New. Series: Systems & Control: Foundations and Applications. Num Pages: 446 pages, biography. BIC Classification: PBWL; TJFM; UYA. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 25. Weight in Grams: 807. . 1991. 2. Hardback. . . . .

Condición: New. Series: Systems & Control: Foundations and Applications. Num Pages: 446 pages, biography. BIC Classification: PBWL; TJFM; UYA. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 25. Weight in Grams: 807. . 1991. 2. Hardback. . . . . Books ship from the US and Ireland.

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 which is supposed to be approaching the true system during the process of identification. This is why there has been an increasing interest in identification and adaptive control for stochastic systems from both theorists and practitioners.

Hardcover. Condición: Like New. Like New. book.

Condición: As New. Unread book in perfect condition.

Hardcover. Condición: new. Hardcover. 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 which is supposed to be approaching the true system during the process of identification. This is why there has been an increasing interest in identification and adaptive control for stochastic systems from both theorists and practitioners. The basic model that is used to describe systems in this book is ARMAX (or its generalizations) which is widely and successfully used in applications. The opening chapters present preliminaries such as the fundamentals of probability theory and both classical and refined results in martingale limit theory and some facts from linear systems. Subsequ Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 constit.

Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 which is supposed to be approaching the true system during the process of identification. This is why there has been an increasing interest in identification and adaptive control for stochastic systems from both theorists and practitioners.Springer Basel AG in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 452 pp. Englisch.

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 which is supposed to be approaching the true system during the process of identification. This is why there has been an increasing interest in identification and adaptive control for stochastic systems from both theorists and practitioners. 452 pp. Englisch.