Forecasting and analytics with the augmented dynamic adaptive model (adam), Svetunkov, Ivan (lancaster University, U.k.)

Автор: Vouros George A., Andrienko Gennady, Doulkeridis Christos
Название: Big Data Analytics for Time-Critical Mobility Forecasting: From Raw Data to Trajectory-Oriented Mobility Analytics in the Aviation and Maritime Domain
ISBN: 3030451666 ISBN-13(EAN): 9783030451660
Издательство: Springer
Цена: 158380.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Next, Part III describes solutions toward processing and managing big spatio-temporal data, particularly enriching data streams and integrating streamed and archival data to provide coherent views of mobility, and storing of integrated mobility data in large distributed knowledge graphs for efficient query-answering.

Автор: Mike West; Jeff Harrison
Название: Bayesian Forecasting and Dynamic Models
ISBN: 1475770987 ISBN-13(EAN): 9781475770988
Издательство: Springer
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Цена: 93160.00 T
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Описание: This text is concerned with Bayesian learning, inference and forecasting in dynamic environments.

Автор: Chi Ronghu, Lin Na, Zhang Huimin
Название: Discrete-Time Adaptive Iterative Learning Control: From Model-Based to Data-Driven
ISBN: 9811904634 ISBN-13(EAN): 9789811904639
Издательство: Springer
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Цена: 121110.00 T
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Описание: The discrete-time adaptive iterative learning control (DAILC) is discussed as a cutting-edge of ILC and can address random initial states, iteration-varying targets, and other non-repetitive uncertainties in practical applications.

Автор: Chi
Название: Discrete-Time Adaptive Iterative Learning Control
ISBN: 9811904669 ISBN-13(EAN): 9789811904660
Издательство: Springer
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Цена: 121110.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book belongs to the subject of control and systems theory. The discrete-time adaptive iterative learning control (DAILC) is discussed as a cutting-edge of ILC and can address random initial states, iteration-varying targets, and other non-repetitive uncertainties in practical applications. This book begins with the design and analysis of model-based DAILC methods by referencing the tools used in the discrete-time adaptive control theory. To overcome the extreme difficulties in modeling a complex system, the data-driven DAILC methods are further discussed by building a linear parametric data mapping between two consecutive iterations. Other significant improvements and extensions of the model-based/data-driven DAILC are also studied to facilitate broader applications. The readers can learn the recent progress on DAILC with consideration of various applications. This book is intended for academic scholars, engineers and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.

Автор: Mario Faliva; Maria Grazia Zoia
Название: Dynamic Model Analysis
ISBN: 3642099483 ISBN-13(EAN): 9783642099489
Издательство: Springer
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Цена: 111790.00 T
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Описание: This monograph provides an insightful analysis of dynamic modeling in econometrics by bridging the structural with the time series approaches, and by focusing on representation theorems of integrated processes.