Автор: Gosavi, Abhijit Название: Simulation-Based Optimization ISBN: 1489974903 ISBN-13(EAN): 9781489974907 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Background.- Simulation basics.- Simulation optimization: an overview.- Response surfaces and neural nets.- Parametric optimization.- Dynamic programming.- Reinforcement learning.- Stochastic search for controls.- Convergence: background material.- Convergence: parametric optimization.- Convergence: control optimization.- Case studies.
Описание: Dr.-Ing. Hao Luo demonstrates the developments of advanced plug-and-play (PnP) process monitoring and control systems for industrial automation processes. With aid of the so-called Youla parameterization, a novel PnP process monitoring and control architecture (PnP-PMCA) with modularized components is proposed. To validate the developments, a case study on an industrial rolling mill benchmark is performed, and the real-time implementation on a laboratory brushless DC motor is presented.
Автор: Thomas Bartz-Beielstein; Bogdan Filipi?; Peter Kor Название: High-Performance Simulation-Based Optimization ISBN: 3030187632 ISBN-13(EAN): 9783030187637 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book presents the state of the art in designing high-performance algorithms that combine simulation and optimization in order to solve complex optimization problems in science and industry, problems that involve time-consuming simulations and expensive multi-objective function evaluations. As traditional optimization approaches are not applicable per se, combinations of computational intelligence, machine learning, and high-performance computing methods are popular solutions. But finding a suitable method is a challenging task, because numerous approaches have been proposed in this highly dynamic field of research.
That’s where this book comes in: It covers both theory and practice, drawing on the real-world insights gained by the contributing authors, all of whom are leading researchers. Given its scope, if offers a comprehensive reference guide for researchers, practitioners, and advanced-level students interested in using computational intelligence and machine learning to solve expensive optimization problems.
Описание: The book provides suggestions on how to start using bionic optimization methods, including pseudo-code examples of each of the important approaches and outlines of how to improve them.
Автор: Abhijit Gosavi Название: Simulation-Based Optimization ISBN: 1489977317 ISBN-13(EAN): 9781489977311 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques - especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms.
Key features of this revised and improved Second Edition include:
- Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search and meta-heuristics (simulated annealing, tabu search, and genetic algorithms)
- Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics
- An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata
- A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online) and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations
Themed around three areas in separate sets of chapters - Static Simulation Optimization, Reinforcement Learning and Convergence Analysis- this book is written for researchers and students in the fields of engineering (industrial, systems, electrical and computer), operations research, computer science and applied mathematics.
Автор: Silja Meyer-Nieberg; Nadiia Leopold; Tobias Uhlig Название: Natural Computing for Simulation-Based Optimization and Beyond ISBN: 3030262146 ISBN-13(EAN): 9783030262143 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This SpringerBrief bridges the gap between the areas of simulation studies on the one hand, and optimization with natural computing on the other.
Описание: The selected articles broadly cover topics on massively parallel and high-performance simulations, modeling and simulation, model development and analysis, performance optimization, power estimation and optimization, high performance computing, reliability, performance analysis, and network simulations.
Описание: This book constitutes the referred proceedings of two workshops held at the 32nd ACM International Conference on Supercomputing, ACM ICS 2018, in Beijing, China, in June 2018. This volume presents the papers that have been accepted for the following workshops: Second International Workshop on High Performance Computing for Advanced Modeling and Simulation in Nuclear Energy and Environmental Science, HPCMS 2018, and First International Workshop on HPC Supported Data Analytics for Edge Computing, HiDEC 2018.
The 20 full papers presented during HPCMS 2018 and HiDEC 2018 were carefully reviewed and selected from numerous submissions. The papers reflect such topics as computing methodologies; parallel algorithms; simulation types and techniques; machine learning.
Описание: This book discusses the parametric modeling, performance evaluation, design optimization and comparative study of the high-speed, parallel pick-and-place robots. The book is of interest to researchers, R&D engineers and graduate students in industrial parallel robotics who wish to learn the core principles, methods, algorithms, and applications.
Описание: This volume contains the edited texts of the lectures presented at the Workshop on High Performance Algorithms and Software for Nonlinear Optimization held in Erice, Sicily, at the "G.
Описание: This book constitutes the thoroughly refereed proceedings of the 5th International Workshop, PMBS 2014 in New Orleans, LA, USA in November 2014. The 12 full and 2 short papers presented in this volume were carefully reviewed and selected from 53 submissions. The papers cover topics on performance benchmarking and optimization;
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
