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[ author_sort:"rafajlowicz, wojciech." ]
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Learning decision sequences for repetitive processes-selected algorithms
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Learning decision sequences for repetitive processes-selected algorithmsby Wojciech Rafajlowicz.
作者:
Rafajlowicz, Wojciech.
出版者:
Cham :Springer International Publishing :2022.
面頁冊數:
xi, 126 p. :ill. (some col.), digital ;24 cm.
Contained By:
Springer Nature eBook
標題:
Mathematical optimization.
電子資源:
https://doi.org/10.1007/978-3-030-88396-6
ISBN:
9783030883966$q(electronic bk.)
Learning decision sequences for repetitive processes-selected algorithms
Rafajlowicz, Wojciech.
Learning decision sequences for repetitive processes-selected algorithms
[electronic resource] /by Wojciech Rafajlowicz. - Cham :Springer International Publishing :2022. - xi, 126 p. :ill. (some col.), digital ;24 cm. - Studies in systems, decision and control,v. 4012198-4190 ;. - Studies in systems, decision and control ;v.3..
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.
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.
ISBN: 9783030883966$q(electronic bk.)
Standard No.: 10.1007/978-3-030-88396-6doiSubjects--Topical Terms:
183292
Mathematical optimization.
LC Class. No.: QA402.5 / .R34 2022
Dewey Class. No.: 519.6
Learning decision sequences for repetitive processes-selected algorithms
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