Learning decision sequences for repetitive processes--selected algorithms / Wojciech Rafajłowicz.
2022
QA402.5 .R34 2022
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Online Access
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Authorized users
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Can lend chapters, not whole ebooks
Details
Title
Learning decision sequences for repetitive processes--selected algorithms / Wojciech Rafajłowicz.
ISBN
9783030883966 (electronic bk.)
3030883965 (electronic bk.)
9783030883959
3030883957
3030883965 (electronic bk.)
9783030883959
3030883957
Published
Cham : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource (132 pages) : illustrations (some color)
Other Standard Identifiers
10.1007/978-3-030-88396-6 doi
Call Number
QA402.5 .R34 2022
Dewey Decimal Classification
519.6
Summary
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 sequencesrelying 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 repetitionsthis 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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Description based on print version record.
Series
Studies in systems, decision and control ; v. 401.
Available in Other Form
Learning Decision Sequences for Repetitive Processes--Selected Algorithms.
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Online Access
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Table of Contents
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.
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.