Generating correlation matrices for normal random vectors in NORTA algorithm using artificial neural networks

Generating multivariate random vectors is a crucial part of the input analysis involved in discrete-event stochastic simulation modeling of multivariate systems. The NORmal-To-Anything (NORTA) algorithm, in which generating the correlation matrices of normal random vectors is the most important task, is one of the most efficient methods in this area. In this algorithm, we need to solve the so-called correlation-matching problem in which some complicated equations that are defined to obtain the correlation matrix of normal random variables need to be solved. Many researchers have tried to solve these equations by three general approaches of (1) solving nonlinear equations analytically, (2) solving equations numerically, and (3) solving equations by simulation. This paper suggests the use of artificial neural networks, called Perceptron, to solve the corresponding problem. Using three simulation experiments, the applicability of the proposed methodology is described and the results obtained from the proposed method to the ones from solving the equations numerically are compared. The results of the simulation experiments show that the proposed method works well.