Population-Based Approaches to the Resource-Constrained and Discrete-Continuous Scheduling, Ewa Ratajczak-Ropel; Aleksander Skakovski

Автор: Ewa Ratajczak-Ropel; Aleksander Skakovski
Название: Population-Based Approaches to the Resource-Constrained and Discrete-Continuous Scheduling
ISBN: 3319628925 ISBN-13(EAN): 9783319628929
Издательство: Springer
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Цена: 130430.00 T
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Описание: This book addresses two of the most difficult and computationally intractable classes of problems: discrete resource constrained scheduling, and discrete-continuous scheduling.

Автор: Rituparna Datta; Kalyanmoy Deb
Название: Evolutionary Constrained Optimization
ISBN: 8132221834 ISBN-13(EAN): 9788132221838
Издательство: Springer
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Цена: 139310.00 T
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Описание: This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms.

Автор: Amnon Meisels
Название: Distributed Search by Constrained Agents
ISBN: 1849967105 ISBN-13(EAN): 9781849967105
Издательство: Springer
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Цена: 130430.00 T
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Описание: The well defined model of distributed constraints satisfaction and optimization (DisCSPs/DisCOPs) can serve as the basis for the design and investigation of distributed search algorithms, of protocols and of negotiations and search.

Автор: Rituparna Datta; Kalyanmoy Deb
Название: Evolutionary Constrained Optimization
ISBN: 8132235053 ISBN-13(EAN): 9788132235057
Издательство: Springer
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Цена: 121890.00 T
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Описание: This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms.

Автор: Todd Hester
Название: TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains
ISBN: 3319011677 ISBN-13(EAN): 9783319011677
Издательство: Springer
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Цена: 130610.00 T
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Описание: This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. It presents a novel model-based reinforcement learning algorithm.

Автор: Park
Название: Recent Advances in Control and Filtering of Dynamic Systems with Constrained Signals
ISBN: 3319962019 ISBN-13(EAN): 9783319962016
Издательство: Springer
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Цена: 130430.00 T
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Описание: This book discusses how to deal with such constraints to guarantee the system`s design objectives, focusing on real-world dynamical systems such as Markovian jump systems, networked control systems, neural networks, and complex networks, which have recently excited considerable attention.

Автор: Todd Hester
Название: TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains
ISBN: 3319375105 ISBN-13(EAN): 9783319375106
Издательство: Springer
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Цена: 104480.00 T
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Описание: This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. It presents a novel model-based reinforcement learning algorithm.

Автор: Ju H. Park; Hao Shen; Xiao-Heng Chang; Tae H. Lee
Название: Recent Advances in Control and Filtering of Dynamic Systems with Constrained Signals
ISBN: 3030071545 ISBN-13(EAN): 9783030071547
Издательство: Springer
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Цена: 130430.00 T
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Описание: This book introduces the principle theories and applications of control and filtering problems to address emerging hot topics in feedback systems. With the development of IT technology at the core of the 4th industrial revolution, dynamic systems are becoming more sophisticated, networked, and advanced to achieve even better performance. However, this evolutionary advance in dynamic systems also leads to unavoidable constraints. In particular, such elements in control systems involve uncertainties, communication/transmission delays, external noise, sensor faults and failures, data packet dropouts, sampling and quantization errors, and switching phenomena, which have serious effects on the system’s stability and performance. This book discusses how to deal with such constraints to guarantee the system’s design objectives, focusing on real-world dynamical systems such as Markovian jump systems, networked control systems, neural networks, and complex networks, which have recently excited considerable attention. It also provides a number of practical examples to show the applicability of the presented methods and techniques.This book is of interest to graduate students, researchers and professors, as well as R&D engineers involved in control theory and applications looking to analyze dynamical systems with constraints and to synthesize various types of corresponding controllers and filters for optimal performance of feedback systems.