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Learning Decision Sequences For Repetitive Processes—Selected Algorithms: Studies in Systems, Decision and Control, cartea 401

Autor Wojciech Rafajłowicz
en Limba Engleză Hardback – 26 oct 2021
This book provides tools and algorithms for solving a wide class of optimization tasks by learning from their repetitions. A unified framework is provided for learning algorithms that are based on the stochastic gradient (a golden standard in learning), including random simultaneous perturbations and the response surface the methodology. Original algorithms include model-free learning of short decision sequences as well as long sequences—relying on model-supported gradient estimation. Learning is based on whole sequences of a process observation that are either vectors or images. This methodology is applicable to repetitive processes, covering a wide range from (additive) manufacturing to decision making for COVID-19 waves mitigation. A distinctive feature of the algorithms is learning between repetitions—this idea extends the paradigms of iterative learning and run-to-run control. The main ideas can be extended to other decision learning tasks, not included in this book. The text is written in a comprehensible way with the emphasis on a user-friendly presentation of the algorithms, their explanations, and recommendations on how to select them. The book is expected to be of interest to researchers, Ph.D., and graduate students in computer science and engineering, operations research, decision making, and those working on the iterative learning control.
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Specificații

ISBN-13: 9783030883959
ISBN-10: 3030883957
Pagini: 126
Ilustrații: XI, 126 p. 32 illus., 19 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.38 kg
Ediția:1st ed. 2022
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Systems, Decision and Control

Locul publicării:Cham, Switzerland

Cuprins

Introduction.- Basic notions and notations.- Learning decision sequences.- Differential evolution with a population filter.- Decision making for COVID-19 suppression.- Stochastic gradient in learning.- Optimal decision sequences.- Learning from image sequences.

Textul de pe ultima copertă

This book provides tools and algorithms for solving a wide class of optimization tasks by learning from their repetitions. A unified framework is provided for learning algorithms that are based on the stochastic gradient (a golden standard in learning), including random simultaneous perturbations and the response surface the methodology. Original algorithms include model-free learning of short decision sequences as well as long sequences—relying on model-supported gradient estimation. Learning is based on whole sequences of a process observation that are either vectors or images. This methodology is applicable to repetitive processes, covering a wide range from (additive) manufacturing to decision making for COVID-19 waves mitigation. A distinctive feature of the algorithms is learning between repetitions—this idea extends the paradigms of iterative learning and run-to-run control. The main ideas can be extended to other decision learning tasks, not included in this book. The text is written in a comprehensible way with the emphasis on a user-friendly presentation of the algorithms, their explanations, and recommendations on how to select them. The book is expected to be of interest to researchers, Ph.D., and graduate students in computer science and engineering, operations research, decision making, and those working on the iterative learning control.

Caracteristici

Provides tools and algorithms for solving a wide class of optimization tasks by learning from their repetitions Includes unified framework for learning algorithms that are based on the stochastic gradient Written in a comprehensible way with the emphasis on a user-friendly presentation of the algorithms, their explanations