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Full Description
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 cools
The conceptual framework necessary to understand current trends in stochastic control, data mining, machine learning, and robotics.
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
