Articoli correlati a Learning Decision Sequences for Repetitive Processes:...

Learning Decision Sequences for Repetitive Processes: Selected Algorithms: 401 - Rilegato

Rafajlowicz, Wojciech

 
9783030883959: Learning Decision Sequences for Repetitive Processes: Selected Algorithms: 401
Rilegato
ISBN 10: 3030883957 ISBN 13: 9783030883959
Casa editrice: Springer Nature, 2021

Sinossi

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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Dalla quarta di copertina

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.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer Nature
Data di pubblicazione
2021
Lingua
Inglese
ISBN 10 
3030883957
ISBN 13 
9783030883959
Rilegatura
Copertina rigida
Numero edizione
1
Numero di pagine
140
Contatto del produttore
ProductSafety@springernature.com
ProductSafety@springernature.com

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