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  2. Continuous simulation - Wikipedia

    en.wikipedia.org/wiki/Continuous_simulation

    Continuous dynamic systems can only be captured by a continuous simulation model, while discrete dynamic systems can be captured either in a more abstract manner by a continuous simulation model (like the Lotka-Volterra equations for modeling a predator-prey eco-system) or in a more realistic manner by a discrete event simulation model (in a ...

  3. Discrete-event simulation - Wikipedia

    en.wikipedia.org/wiki/Discrete-event_simulation

    A discrete-event simulation (DES) models the operation of a system as a sequence of events in time. Each event occurs at a particular instant in time and marks a change of state in the system. [ 1 ] Between consecutive events, no change in the system is assumed to occur; thus the simulation time can directly jump to the occurrence time of the ...

  4. Discrete time and continuous time - Wikipedia

    en.wikipedia.org/wiki/Discrete_time_and...

    Unlike a continuous-time signal, a discrete-time signal is not a function of a continuous argument; however, it may have been obtained by sampling from a continuous-time signal. When a discrete-time signal is obtained by sampling a sequence at uniformly spaced times, it has an associated sampling rate.

  5. Discrete rate simulation - Wikipedia

    en.wikipedia.org/wiki/Discrete_rate_simulation

    A comparison between discrete rate, continuous, and discrete event simulation. Discrete rate simulation is similar to discrete event simulation in that both methodologies model the operation of the system as a discrete sequence of events in time. However, while discrete event simulation assumes there is no change in the system between ...

  6. Continuous modelling - Wikipedia

    en.wikipedia.org/wiki/Continuous_modelling

    Continuous modelling is the mathematical practice of applying a model to continuous data (data which has a potentially infinite number, and divisibility, of attributes). They often use differential equations [1] and are converse to discrete modelling. Modelling is generally broken down into several steps:

  7. Stochastic simulation - Wikipedia

    en.wikipedia.org/wiki/Stochastic_simulation

    If we necessarily need to answer all the questions, or if we don't know what purposes is the model going to be used for, it is convenient to apply combined continuous/discrete methodology. [20] Similar techniques can change from a discrete, stochastic description to a deterministic, continuum description in a time-and space dependent manner. [21]

  8. Dynamical system - Wikipedia

    en.wikipedia.org/wiki/Dynamical_system

    A discrete-time, affine dynamical system has the form of a matrix difference equation: + = +, with A a matrix and b a vector. As in the continuous case, the change of coordinates x → x + (1 − A) –1 b removes the term b from the equation.

  9. Discretization - Wikipedia

    en.wikipedia.org/wiki/Discretization

    Dichotomization is the special case of discretization in which the number of discrete classes is 2, which can approximate a continuous variable as a binary variable (creating a dichotomy for modeling purposes, as in binary classification). Discretization is also related to discrete mathematics, and is an important component of granular computing.