Advanced mathematical tools for automatic control engineers. Volume 2, Stochastic techniques [electronic resource] / Alex Poznyak.Material type: TextPublisher: Amsterdam ; Boston : Elsevier Science, 2009Edition: 1st edDescription: 1 online resource (xxix, 538 p.) : illISBN: 9780080914039 (electronic bk.); 0080914039 (electronic bk.)Other title: Stochastic techniquesSubject(s): Automatic control -- Mathematics | TECHNOLOGY & ENGINEERING -- Automation | TECHNOLOGY & ENGINEERING -- RoboticsGenre/Form: Electronic books.Additional physical formats: Print version:: Advanced mathematical tools for automatic control engineers.DDC classification: 629.8312 LOC classification: TJ212 | .P69 2009ebOnline resources: ScienceDirect
Includes bibliographical references (p. 529-533) and index.
The second volume of this work continues the and approach of the first volume, providing mathematical tools for the control engineer and examining such topics as random variables and sequences, iterative logarithmic and large number laws, differential equations, stochastic measurements and optimization, discrete martingales and probability space. It includes proofs of all theorems and contains many examples with solutions. It is written for researchers, engineers and advanced students who wish to increase their familiarity with different topics of modern and classical mathematics related to system and automatic control theories. It also has applications to game theory, machine learning and intelligent systems. * 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.
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.
Description based on print version record.