Stochastic Learning and Optimization : A Sensitivity-Based Approach

Xi-Ren Cao

ISBN 10: 144194222X / ISBN 13: 9781441942227

Published by Springer US, Springer US, 2010

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9781441942227

Bibliographic Details

• Title: Stochastic Learning and Optimization : A Sensitivity-Based Approach
• Publisher: Springer US, Springer US
• Publication Date: 2010
• Language: English
• ISBN 10: 144194222X
• ISBN 13: 9781441942227
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 32D millimeters 235 millimeters width by 155 millimeters height by 32 millimeters depth
• Weight: 879 grams

About this title

Synopsis

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.

From the Back Cover

Stochastic learning and optimization is a multidisciplinary subject that has wide applications in modern engineering, social, and financial problems, including those in Internet and wireless communications, manufacturing, robotics, logistics, biomedical systems, and investment science.  This book is unique in the following aspects.

1. (Four areas in one book)  This book covers various disciplines in learning and optimization, including perturbation analysis (PA) of discrete-event dynamic systems, Markov decision processes (MDP)s), reinforcement learning (RL), and adaptive control, within a unified framework.
2. (A simple approach to MDPs) This book introduces MDP theory through a simple approach based on performance difference formulas.  This approach leads to results for the n-bias optimality with long-run average-cost criteria and Blackwell's optimality without discounting.
3. (Event-based optimization) This book introduces the recently developed event-based optimization approach, which opens up a research direction in overcoming or alleviating the difficulties due to the curse of dimensionality issue by utilizing the system's special features.
4. (Sample-path construction) This book emphasizes physical interpretations based on the sample-path construction.