AI Time Series Control System Modelling / by Chuzo Ninagawa

By:

Ninagawa, Chuzo [VerfasserIn]

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Publisher: Singapore : Springer Nature Singapore, 2023Publisher: Singapore : Imprint: Springer, 2023Edition: 1st ed. 2023Description: 1 Online-Ressource(XI, 237 p. 192 illus., 1 illus. in color.)ISBN:

9789811945946

Subject(s):

Additional physical formats: 9789811945939 | 9789811945953 | 9789811945960 | Erscheint auch als: 9789811945939 Druck-Ausgabe | Erscheint auch als: 9789811945953 Druck-Ausgabe | Erscheint auch als: 9789811945960 Druck-AusgabeDOI: DOI: 10.1007/978-981-19-4594-6Online resources:

Summary: Introduction -- Linear Time Series Modeling -- Deep Learning AI Modeling -- LSTM AI Modeling -- Optimal Control by Time-Series AI Model -- The Reality of Time Series Learning Data Collection -- Practical Work on Time Series AI Modeling.Summary: This book describes the practical application of artificial intelligence (AI) methods using time series data in system control. This book consistently discusses the application of machine learning to the analysis and modelling of time series data of physical quantities to be controlled in the field of system control. Since dynamic systems are not stable steady states but changing transient states, the changing transient states depend on the state history before the change. In other words, it is essential to predict the change from the present to the future based on the time history of each variable in the target system, and to manipulate the system to achieve the desired change. In short, time series is the key to the application of AI machine learning to system control. This is the philosophy of this book: "time series data" + "AI machine learning" = "new practical control methods". This book can give my helps to undergradate or graduate students, institute researchers and senior engineers whose scientific background are engineering, mathematics, physics and other natural sciences.PPN: PPN: 1815864133Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-INR | ZDB-2-SXIT

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