國立虎尾科技大學 |

Learning Decision Sequences For Repetitive Processes—Selected Algorithms

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Learning Decision Sequences For Repetitive Processes—Selected Algorithms/ by Wojciech Rafajłowicz.
作者:	Rafajłowicz, Wojciech.
面頁冊數:	XI, 126 p. 32 illus., 19 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Machine Learning. -
電子資源:	https://doi.org/10.1007/978-3-030-88396-6
ISBN:	9783030883966

Learning Decision Sequences For Repetitive Processes—Selected Algorithms
Rafajłowicz, Wojciech.

Learning Decision Sequences For Repetitive Processes—Selected Algorithms[electronic resource] /by Wojciech Rafajłowicz. - 1st ed. 2022. - XI, 126 p. 32 illus., 19 illus. in color.online resource. - Studies in Systems, Decision and Control,4012198-4190 ;. - Studies in Systems, Decision and Control,27.

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

Standard No.: 10.1007/978-3-030-88396-6doiSubjects--Topical Terms:

1137723
Machine Learning.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Learning Decision Sequences For Repetitive Processes—Selected Algorithms
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