Open AccessPhase Enlargement of Semi-Markov Systems without Determining Stationary Distribution of Embedded Markov Chain
Author(s) -
Вадим Яковлевич Копп,
Михаил Вадимович Заморёнов,
Никита Игоревич Чаленков,
Иван Александрович Скатков
Publication year - 2020
Publication title -
trudy spiiran
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.242
H-Index - 9
eISSN - 2078-9599
pISSN - 2078-9181
DOI - 10.15622/sp.2020.19.3.3
Subject(s) - markov chain , mathematics , balance equation , random variable , markov process , lemma (botany) , markov property , stationary distribution , markov model , markov renewal process , mathematical analysis , continuous time markov chain , markov chain mixing time , markov kernel , discrete phase type distribution , variable order markov model , statistics , ecology , poaceae , biology
A phase enlargement of semi-Markov systems that does not require determining stationary distribution of the embedded Markov chain is considered. Phase enlargement is an equivalent replacement of a semi-Markov system with a common phase state space by a system with a discrete state space. Finding the stationary distribution of an embedded Markov chain for a system with a continuous phase state space is one of the most time-consuming and not always solvable stage, since in some cases it leads to a solution of integral equations with kernels containing sum and difference of variables.
For such equations there is only a particular solution and there are no general solutions to date. For this purpose a lemma on a type of a distribution function of the difference of two random variables, provided that the first variable is greater than the subtracted variable, is used.
It is shown that the type of the distribution function of difference of two random variables under the indicated condition depends on one constant, which is determined by a numerical method of solving the equation presented in the lemma.
Based on the lemma, a theorem on the difference of a random variable and a complicated recovery flow is built up. The use of this method is demonstrated by the example of modeling a technical system consisting of two series-connected process cells, provided that both cells cannot fail simultaneously. The distribution functions of the system residence times in enlarged states, as well as in a subset of working and non-working states, are determined. The simulation results are compared by the considered and classical method proposed by V. Korolyuk, showed the complete coincidence of the sought quantities.