we compare two approaches for a markovian model in flexible manufacturing systems (fmss) using monte carlo simulation. the model which is a development of fazlollahtabar and saidi-mehrabad (2013), considers two features of automated flexible manufacturing systems equipped with automated guided vehicle (agv) namely, the reliability of machines and the reliability of agvs in a multiple agv jobsho...

Sensitivity analysis of Markovian models amounts to computing the constants in polynomial functions of a parameter under study. To handle the computational complexity involved, we propose a method for approximate sensitivity analysis of such models. We show that theoretical properties allow us to reason for the present time using just few observations from the past with small loss in accuracy. ...

Exponential synchronization of Markovian jumping neural networks with partly unknown transition probabilities via stochastic sampled-data control, Neurocomputing, This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review...

The stochastic reaction diffusion systems may suffer sudden shocks‎, ‎in order to explain this phenomena‎, ‎we use Markovian jumps to model stochastic reaction diffusion systems‎. ‎In this paper‎, ‎we are interested in almost sure exponential stability of stochastic reaction diffusion systems with Markovian jumps‎. ‎Under some reasonable conditions‎, ‎we show that the trivial solution of stocha...

This paper focuses on the finite time fault tolerant state estimation of Markovian jumping neural networks with two delay components. Firstly, mathematical expression estimator is defined when system components have faults and output has external disturbances. Then, an augmented Lyapunov-Krasovslii functional including additive information, information activation function used to derive stochas...

This paper is concerned with the event-triggered state estimation (ETSE) problem for a class of discrete-time Markovian jumping neural networks mode-dependent time-delays and uncertain transition probabilities. The parameters experience switches that are characterized by chain whose probabilities allowed to be uncertain. mechanism introduced in sensor-to-estimator channel reduce frequency signa...