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.
Описание: This book presents four typical motion planning schemes based on optimization techniques, including the fundamental RMP scheme and its extensions. These schemes are unified as quadratic programs, which are solved by neural networks or numerical algorithms.
Описание: This book presents four typical motion planning schemes based on optimization techniques, including the fundamental RMP scheme and its extensions. These schemes are unified as quadratic programs, which are solved by neural networks or numerical algorithms.
Автор: Gusfield, Dan Название: Algorithms on strings, trees and sequences ISBN: 0521585198 ISBN-13(EAN): 9780521585194 Издательство: Cambridge Academ Рейтинг: Цена: 13147.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book describes a range of string problems in computer science and molecular biology and the algorithms developed to solve them.
Автор: Kai-Uwe Schmidt; Arne Winterhof Название: Sequences and Their Applications - SETA 2014 ISBN: 3319123246 ISBN-13(EAN): 9783319123240 Издательство: Springer Рейтинг: Цена: 7826.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The papers have been organized in topical sections on Boolean functions, perfect sequences, correlation of arrays, relative difference sets, aperiodic correlation, pseudorandom sequences and stream ciphers, crosscorrelation of sequences, prime numbers in sequences, OFDM and CDMA, and frequency-hopping sequences.
Автор: Allen G. Rodrigo; Gerald H. Learn Jr. Название: Computational and Evolutionary Analysis of HIV Molecular Sequences ISBN: 1475774540 ISBN-13(EAN): 9781475774542 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Computational and Evolutionary Analysis of HIV Molecular Sequences is for all researchers interested in HIV research, even those who only have a nodding acquaintance with computational biology (or those who are familiar with some, but not all, aspects of the field).
Автор: Hyeong Soo Chang; Michael C. Fu; Jiaqiao Hu; Steve Название: Simulation-based Algorithms for Markov Decision Processes ISBN: 1849966435 ISBN-13(EAN): 9781849966436 Издательство: Springer Рейтинг: Цена: 14673.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences.
Автор: Chang Hyeong Soo Название: Simulation-based Algorithms for Markov Decision Processes ISBN: 144715021X ISBN-13(EAN): 9781447150213 Издательство: Springer Рейтинг: Цена: 19591.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The updated 2nd edition of this book covers MDPs in constrained settings and with uncertain transition properties; approximation stochastic annealing, a population-based on-line simulation-based algorithm; game-theoretic method for solving MDPs and more.
Автор: Ankan Ankur, Panda Abinash Название: Hands-On Markov Models with Python ISBN: 1788625447 ISBN-13(EAN): 9781788625449 Издательство: Неизвестно Рейтинг: Цена: 7171.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book will help you become familiar with HMMs and different inference algorithms by working on real-world problems. You will start with an introduction to the basic concepts of Markov chains, Markov processes and then delve deeper into understanding hidden Markov models and its types using practical examples.
Автор: C.Lee Giles; Marco Gori Название: Adaptive Processing of Sequences and Data Structures ISBN: 3540643419 ISBN-13(EAN): 9783540643418 Издательство: Springer Рейтинг: Цена: 8099.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This text is devoted to adaptive processing of structured information similar to flexible and intelligent information processing by humans. It allows for a mixture of sequential and parallel processing of symbolic as well as sub-symbolic information with deterministic and probabilistic frameworks.
Описание: This interdisciplinary reference and guide provides an introduction to modeling methodologies and models which form the starting point for deriving efficient and effective solution techniques, and presents a series of case studies that demonstrate how heuristic and analytical approaches may be used to solve large and complex problems.
Автор: Hyeong Soo Chang; Jiaqiao Hu; Michael C. Fu; Steve Название: Simulation-Based Algorithms for Markov Decision Processes ISBN: 144715990X ISBN-13(EAN): 9781447159902 Издательство: Springer Рейтинг: Цена: 16977.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The updated 2nd edition of this book covers MDPs in constrained settings and with uncertain transition properties; approximation stochastic annealing, a population-based on-line simulation-based algorithm; game-theoretic method for solving MDPs and more.
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