Advanced Mathematical Tools for Automatic Control Engineers: Volume 2

Stochastic Systems

Nonfiction, Science & Nature, Mathematics, Applied, Technology, Engineering, Mechanical
Cover of the book Advanced Mathematical Tools for Automatic Control Engineers: Volume 2 by Alex Poznyak, Elsevier Science
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Author: Alex Poznyak ISBN: 9780080914039
Publisher: Elsevier Science Publication: August 13, 2009
Imprint: Elsevier Science Language: English
Author: Alex Poznyak
ISBN: 9780080914039
Publisher: Elsevier Science
Publication: August 13, 2009
Imprint: Elsevier Science
Language: English

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
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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