Taking Charge of Your Career : The Essential Guide to Finding the Job That's Right for You
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Bester Preis: Fr. 13.48 (€ 13.78)¹ (vom 30.07.2017)1
Learning-Based Adaptive Control (2016)
EN NW EB DLISBN: 9780128031513 bzw. 0128031514, in Englisch, Butterworth-Heinemann, Butterworth-Heinemann, Butterworth-Heinemann, neu, E-Book, elektronischer Download.
Lieferung aus: Deutschland, in-stock.
Adaptive control has been one of the main problems studied in control theory. The subject is well understood, yet it has a very active research frontier. This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control. As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change. This book offers a new perspective about how to deal with these variations. By merging together Model-Free and Model-Based learning algorithms, the author demonstrates, using a number of mechatronic examples, how the learning process can be shortened and optimal control performance can be reached and maintained. Includes a good number of Mechatronics Examples of the techniques. Compares and blends Model-free and Model-based learning algorithms. Covers fundamental concepts, state-of-the-art research, necessary tools for modeling, and control.
Adaptive control has been one of the main problems studied in control theory. The subject is well understood, yet it has a very active research frontier. This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control. As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change. This book offers a new perspective about how to deal with these variations. By merging together Model-Free and Model-Based learning algorithms, the author demonstrates, using a number of mechatronic examples, how the learning process can be shortened and optimal control performance can be reached and maintained. Includes a good number of Mechatronics Examples of the techniques. Compares and blends Model-free and Model-based learning algorithms. Covers fundamental concepts, state-of-the-art research, necessary tools for modeling, and control.
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Learning-Based Adaptive Control, An Extremum Seeking Approach – Theory and Applications (2016)
EN NW EBISBN: 9780128031513 bzw. 0128031514, in Englisch, Butterworth-Heinemann, neu, E-Book.
Lieferung aus: Niederlande, Direct beschikbaar.
bol.com.
Adaptive control has been one of the main problems studied in control theory. The subject is well understood, yet it has a very active research frontier. This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control. As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change. This book offers a new perspective about how to deal with these variations. By merging together Model-Free... Adaptive control has been one of the main problems studied in control theory. The subject is well understood, yet it has a very active research frontier. This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control. As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change. This book offers a new perspective about how to deal with these variations. By merging together Model-Free and Model-Based learning algorithms, the author demonstrates, using a number of mechatronic examples, how the learning process can be shortened and optimal control performance can be reached and maintained. Includes a good number of Mechatronics Examples of the techniques.Compares and blends Model-free and Model-based learning algorithms.Covers fundamental concepts, state-of-the-art research, necessary tools for modeling, and control.Taal: Engels;Formaat: ePub met kopieerbeveiliging (DRM) van Adobe;Kopieerrechten: Het kopiëren van (delen van) de pagina's is niet toegestaan ;Geschikt voor: Alle e-readers te koop bij bol.com (of compatible voor PDF of ePub). Telefoons en tablets met Google Android (1.6 of hoger) voorzien van bol.com boekenbol app. PC en Mac;Verschijningsdatum: augustus 2016;ISBN10: 0128031514;ISBN13: 9780128031513; Engelstalig | Ebook | 2016.
bol.com.
Adaptive control has been one of the main problems studied in control theory. The subject is well understood, yet it has a very active research frontier. This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control. As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change. This book offers a new perspective about how to deal with these variations. By merging together Model-Free... Adaptive control has been one of the main problems studied in control theory. The subject is well understood, yet it has a very active research frontier. This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control. As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change. This book offers a new perspective about how to deal with these variations. By merging together Model-Free and Model-Based learning algorithms, the author demonstrates, using a number of mechatronic examples, how the learning process can be shortened and optimal control performance can be reached and maintained. Includes a good number of Mechatronics Examples of the techniques.Compares and blends Model-free and Model-based learning algorithms.Covers fundamental concepts, state-of-the-art research, necessary tools for modeling, and control.Taal: Engels;Formaat: ePub met kopieerbeveiliging (DRM) van Adobe;Kopieerrechten: Het kopiëren van (delen van) de pagina's is niet toegestaan ;Geschikt voor: Alle e-readers te koop bij bol.com (of compatible voor PDF of ePub). Telefoons en tablets met Google Android (1.6 of hoger) voorzien van bol.com boekenbol app. PC en Mac;Verschijningsdatum: augustus 2016;ISBN10: 0128031514;ISBN13: 9780128031513; Engelstalig | Ebook | 2016.
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Learning-Based Adaptive Control - An Extremum Seeking Approach - Theory and Applications
EN NW EB DLISBN: 9780128031513 bzw. 0128031514, in Englisch, Elsevier Reference Monographs, neu, E-Book, elektronischer Download.
Lieferung aus: Deutschland, E-Book zum Download.
Adaptive control has been one of the main problems studied in control theory. The subject is well understood, yet it has a very active research frontier. This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control. As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change. This book offers a new perspective about how to deal with these variations. By merging together Model-Free and Model-Based learning algorithms, the author demonstrates, using a number of mechatronic examples, how the learning process can be shortened and optimal control performance can be reached and maintained. Includes a good number of Mechatronics Examples of the techniques. Compares and blends Model-free and Model-based learning algorithms. Covers fundamental concepts, state-of-the-art research, necessary tools for modeling, and control. Mouhacine Benosman worked at universities in Rome, Italy, Reims, France, and Glasgow, Scotland before spending 5 years as a Research Scientist with the Temasek Laboratories at the National University of Singapore. He is presently senior researcher at the Mitsubishi Electric Research Laboratories (MERL), Cambridge, USA. His research interests include modelling and control of flexible systems, non-linear robust and fault tolerant control, vibration suppression in industrial machines, multi-agent control with applications to smart-grid, and more recently his research focus is on learning and adaptive control with application to mechatronics systems. The author has published more than 40 peer-reviewed journals and conferences, and more than 10 patents in the field of mechatronics systems control. He is a senior member of the IEEE society and an Associate Editor of the Control System Society Conference Editorial Board.
Adaptive control has been one of the main problems studied in control theory. The subject is well understood, yet it has a very active research frontier. This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control. As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change. This book offers a new perspective about how to deal with these variations. By merging together Model-Free and Model-Based learning algorithms, the author demonstrates, using a number of mechatronic examples, how the learning process can be shortened and optimal control performance can be reached and maintained. Includes a good number of Mechatronics Examples of the techniques. Compares and blends Model-free and Model-based learning algorithms. Covers fundamental concepts, state-of-the-art research, necessary tools for modeling, and control. Mouhacine Benosman worked at universities in Rome, Italy, Reims, France, and Glasgow, Scotland before spending 5 years as a Research Scientist with the Temasek Laboratories at the National University of Singapore. He is presently senior researcher at the Mitsubishi Electric Research Laboratories (MERL), Cambridge, USA. His research interests include modelling and control of flexible systems, non-linear robust and fault tolerant control, vibration suppression in industrial machines, multi-agent control with applications to smart-grid, and more recently his research focus is on learning and adaptive control with application to mechatronics systems. The author has published more than 40 peer-reviewed journals and conferences, and more than 10 patents in the field of mechatronics systems control. He is a senior member of the IEEE society and an Associate Editor of the Control System Society Conference Editorial Board.
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Learning-Based Adaptive Control: An Extremum Seeking Approach - Theory and Applications (2016)
EN NW EB DLISBN: 9780128031513 bzw. 0128031514, in Englisch, Butterworth-Heinemann, Butterworth-Heinemann, Butterworth-Heinemann, neu, E-Book, elektronischer Download.
Lieferung aus: Kanada, in-stock.
Learning-Based Adaptive Control: An Extremum Seeking Approach - Theory and Applications.
Learning-Based Adaptive Control: An Extremum Seeking Approach - Theory and Applications.
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Learning-Based Adaptive Control als eBook Download von
EN NW EBISBN: 9780128031513 bzw. 0128031514, in Englisch, Elsevier Science, neu, E-Book.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
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Taking Charge of Your Career : The Essential Guide to Finding the Job That's Right for You
EN NW EB DLISBN: 9781472933362 bzw. 1472933362, in Englisch, Springer New York, neu, E-Book, elektronischer Download.
Lieferung aus: Vereinigtes Königreich Grossbritannien und Nordirland, Despatched same working day before 3pm.
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