AI Time Series Control System Modelling [electronic resource] / by Chuzo Ninagawa.

Por: Ninagawa, Chuzo [author.]Colaborador(es): SpringerLink (Online service)Tipo de material: TextoTextoEditor: Singapore : Springer Nature Singapore : Imprint: Springer, 2023Edición: 1st ed. 2023Descripción: XI, 237 p. 192 illus., 1 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9789811945946Tema(s): Control engineering | Robotics | Automation | Machine learning | Artificial intelligence | System theory | Control theory | Control, Robotics, Automation | Machine Learning | Control and Systems Theory | Artificial Intelligence | Systems Theory, ControlFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 629.8 Clasificación LoC:TJ212-225TJ210.2-211.495Recursos en línea: Libro electrónicoTexto
Contenidos:
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.
En: Springer Nature eBookResumen: 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.
Star ratings
    Valoración media: 0.0 (0 votos)
Existencias
Tipo de ítem Biblioteca actual Colección Signatura Copia número Estado Fecha de vencimiento Código de barras
Libro Electrónico Biblioteca Electrónica
Colección de Libros Electrónicos 1 No para préstamo

Acceso multiusuario

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.

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.

UABC ; Perpetuidad

Con tecnología Koha