Stochastic Systems: Estimation, Identification, and Adaptive Control (Classics in Applied Mathematics, Series Number 75)

Kumar, P. R.

ISBN 10: 1611974259 / ISBN 13: 9781611974256

Published by Society for Industrial and Applied Mathematics, 2016

About this Item

Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes). Seller Inventory # 002068236N

Bibliographic Details

Title: Stochastic Systems: Estimation, ...

Publisher: Society for Industrial and Applied Mathematics

Publication Date: 2016

Binding: paperback

Condition: New

About this title

Synopsis:

Since its origins in the 1940s, the subject of decision making under uncertainty has grown into a diversified area with application in several branches of engineering and in those areas of the social sciences concerned with policy analysis and prescription. These approaches required a computing capacity too expensive for the time, until the ability to collect and process huge quantities of data engendered an explosion of work in the area.

This book provides succinct and rigorous treatment of the foundations of stochastic control; a unified approach to filtering, estimation, prediction, and stochastic and adaptive control; and the conceptual framework necessary to understand current trends in stochastic control, data mining, machine learning, and robotics.

Audience: This book is recommended for those who have been introduced to probability theory and stochastic processes and want to learn more about decision making under uncertainty. It can be used as a one- or two-semester course textbook for advanced undergrad or first-year graduate students.

Contents: Chapter 1: Introduction; Chapter 2: State space models; Chapter 3: Properties of linear stochastic systems; Chapter 4: Controlled Markov chain model; Chapter 5: Input output models; Chapter 6: Dynamic programming; Chapter 7: Linear systems: estimation and control; Chapter 8: Infinite horizon dynamic programming; Chapter 9: Introduction to system identification; Chapter 10: Linear system identification; Chapter 11: Bayesian adaptive control; Chapter 12: Non-Bayesian adaptive control; Chapter 13: Self-tuning regulators for linear systems

Book Description:

A succinct and rigorous treatment of the foundations of stochastic control, ideal for advanced students already acquainted with stochastic processes. This book presents a unified approach to filtering, estimation, prediction, and stochastic and adaptive control, while also providing the conceptual framework necessary to understand current trends.