Rafaj?owicz Learning Decision Sequences For Repetitive Processes

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Автор: Rafaj?owicz
Название: Learning Decision Sequences For Repetitive Processes—Selected Algorithms
ISBN: 9783030883980
Издательство: Springer
Классификация:

Алгоритмы и структуры данных

Искусственный интеллект

Машинное обучение

ISBN-10: 3030883981
Обложка/Формат: Soft cover
Страницы: 126
Вес: 0.23 кг.
Дата издания: 10.11.2022
Серия: Studies in Systems, Decision and Control
Язык: English
Издание: 1st ed. 2022
Иллюстрации: 19 illustrations, color; 13 illustrations, black and white; xi, 126 p. 32 illus., 19 illus. in color.
Размер: 235 x 155
Читательская аудитория: Professional & vocational
Основная тема: Engineering
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: 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.
Дополнительное описание: Introduction.- Basic notions and notations.- Learning decision sequences.- Di?erential evolution with a population ?lter.- Decision making for COVID-19 suppression.- Stochastic gradient in learning.- Optimal decision sequences.- Learning from image sequen

Learning Decision Sequences For Repetitive Processes—Selected Algorithms, Rafaj?owicz