Freshly Printed - allow 10 days lead
Couldn't load pickup availability
Semi-Markov Models
Control of Restorable Systems with Latent Failures
Modeling a wide class of systems using SMP with a common phase space of states.
Yuriy E Obzherin (Author), Elena G Boyko (Author)
9780128022122, Elsevier Science
Paperback, published 2 February 2015
212 pages
22.9 x 15.1 x 1.5 cm, 0.43 kg
"The book can be recommended to readers interested in building mathematical models for control strategies in engineering systems and technological processes." --Zentralblatt MATH
Featuring previously unpublished results, Semi-Markov Models: Control of Restorable Systems with Latent Failures describes valuable methodology which can be used by readers to build mathematical models of a wide class of systems for various applications. In particular, this information can be applied to build models of reliability, queuing systems, and technical control. Beginning with a brief introduction to the area, the book covers semi-Markov models for different control strategies in one-component systems, defining their stationary characteristics of reliability and efficiency, and utilizing the method of asymptotic phase enlargement developed by V.S. Korolyuk and A.F. Turbin. The work then explores semi-Markov models of latent failures control in two-component systems. Building on these results, solutions are provided for the problems of optimal periodicity of control execution. Finally, the book presents a comparative analysis of analytical and imitational modeling of some one- and two-component systems, before discussing practical applications of the results
List of Notations and Abbreviations Introduction Chapter 1: Preliminaries Chapter 2: Semi-Markov Models of One-Component Systems with Regard to Control of Latent FailuresChapter 3: Semi-Markov Models of Two-Component Systems with Regard to Control of Latent Failures Chapter 4: Optimization of Execution Periodicity of Latent Failures Control Chapter 5: Application and Verification of the Results
Subject Areas: Stochastics [PBWL], Probability & statistics [PBT]
