Alexander S. Poznyak

Advanced Mathematical Tools for Automatic Control Engineers: Volume 2 (eBook, ePUB)

Stochastic Systems

• Format: ePub

Produktbeschreibung

Advanced Mathematical Tools for Automatic Control Engineers, Volume 2: Stochastic Techniques provides comprehensive discussions on statistical tools for control engineers.

The book is divided into four main parts. Part I discusses the fundamentals of probability theory, covering probability spaces, random variables, mathematical expectation, inequalities, and characteristic functions. Part II addresses discrete time processes, including the concepts of random sequences, martingales, and limit theorems. Part III covers continuous time stochastic processes, namely Markov processes, stochastic integrals, and stochastic differential equations. Part IV presents applications of stochastic techniques for dynamic models and filtering, prediction, and smoothing problems. It also discusses the stochastic approximation method and the robust stochastic maximum principle.

• Provides comprehensive theory of matrices, real, complex and functional analysis
• Provides practical examples of modern optimization methods that can be effectively used in variety of real-world applications
• Contains worked proofs of all theorems and propositions presented

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 567
• Erscheinungstermin: 13. August 2009
• Englisch
• ISBN-13: 9780080914039
• Artikelnr.: 38014215

Autorenporträt

Alexander Poznyak is Professor and Department Head of Automatic Control at CINESTAV of IPN in Mexico. He graduated from Moscow Physical Technical Institute in 1970, and earned Ph.D. and Doctoral Degrees from the Institute of Control Sciences of Russian Academy of Sciences in 1978 and 1989, respectively. He has directed 43 Ph.D. theses, and published more than 260 papers and 14 books.

Inhaltsangabe

Preface
Introduction
Probability Space
Random Variables
Mathematical Expectation
Random Sequences
Conditional Mathematical Expectation
Discrete Martingales
Large Number Laws
Characteristic Functions and the Central Limit Theorem
Iterative Logarithmic Law
Stochastic Differential Equations
Wiener and Kalman Filtering
Parametric Identification under Stochastic Measurements
Stochastic Optimization
Finite Markov Chains, Discrete Events and Elements of Queering Theory