Abstract:
A system with an occurrence is a DES (discrete event system) with discrete
conditions, which means that its condition evolution is solely dependent on
discrete, contradictory events [1]. A methodology for simulating various sys-
tems that may be split into several logically independent processes that move
through time on their own is defined as discrete event simulation (DES). Each
event has a logical timing and occurs on a specific process (a timestamp). This
event’s output can be sent to one or more other processes. A new set of oc-
currences that should be handled at a future meaningful time may be created
because of the outcome’s content. Typically, f(x) in a CVDS represents the an-
swer to the setup of equations p(y)=a(p(y), b(y), y). When an event happens,
a(y) is a piece-wise uniform function because the condition of a discrete event
system changes from one discrete value to another. Condition that monitoring
problem for a certain kind of neural network is solved using Markovian jump-
ing parameters, which is the subject of this paper. The modes of the neural net-
work models can switch between each other within a Markov chain and have
a finite number of modes. In a discrete-time M.C, the events occur at specific
time periods, denoted as {0, 1, 2, 3, . . . , k}. This allows us to construct a stochas-
tic sequence {X1, X2, X3, . . . }, where the defining characteristic is its Markov
property.
P [Xp
+ 1 =Xp
+ 1|Xp
= Xp, Xp − 1 = Xp − 1, ..., X0 = x0]
=P [X − k + 1 = Xp
+ 1|Xp
= Xp
] (1.1)
