Cao Stochastic Learning and Optimization
1. Auflage 2007
ISBN: 978-0-387-69082-7
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark
A Sensitivity-Based Approach
E-Book, Englisch, 566 Seiten, eBook
ISBN: 978-0-387-69082-7
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark
Performance optimization is vital in the design and operation of modern engineering systems, including communications, manufacturing, robotics, and logistics. Most engineering systems are too complicated to model, or the system parameters cannot be easily identified, so learning techniques have to be applied.
This is a multi-disciplinary area which has been attracting wide attention across many disciplines. Areas such as perturbation analysis (PA) in discrete event dynamic systems (DEDSs), Markov decision processes (MDPs) in operations research, reinforcement learning (RL) or neuro-dynamic programming (NDP) in computer science, identification and adaptive control (I&AC) in control systems, share the common goal: to make the "best decision" to optimize system performance.
This book provides a unified framework based on a sensitivity point of view. It also introduces new approaches and proposes new research topics within this sensitivity-based framework.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
Four Disciplines in Learning and Optimization.- Perturbation Analysis.- Learning and Optimization with Perturbation Analysis.- Markov Decision Processes.- Sample-Path-Based Policy Iteration.- Reinforcement Learning.- Adaptive Control Problems as MDPs.- The Event-Based Optimization - A New Approach.- Event-Based Optimization of Markov Systems.- Constructing Sensitivity Formulas.
