| 书目名称 | Learning Decision Sequences For Repetitive Processes—Selected Algorithms |
| 编辑 | Wojciech Rafajłowicz |
| 视频video | |
| 概述 | 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 grad |
| 丛书名称 | Studies in Systems, Decision and Control |
| 图书封面 |  |
| 描述 | 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 interes |
| 出版日期 | Book 2022 |
| 关键词 | Optimization; Algorithms; Decision Making; Control; Automation |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-88396-6 |
| isbn_softcover | 978-3-030-88398-0 |
| isbn_ebook | 978-3-030-88396-6Series ISSN 2198-4182 Series E-ISSN 2198-4190 |
| issn_series | 2198-4182 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
发表于 2025-3-21 16:24:24
