An extended stochastic gradient algorithm is developed to estimate the parameters of Hammerstein–Wiener ARMAX models. The basic idea is to replace the unmeasurable noise terms in the information vector of the pseudo-linear regression identification model with the corresponding noise estimates which are computed by the obtained parameter estimates. The obtained parameter estimates of the identif...

Abstract: The problem of estimating a set of parameters in the autoregressive moving average model with exogenous inputs (ARMAX) is considered and a numerical Bayesian method proposed. This paper, develops a Bayesian analysis for the ARMAX model by implementing a fast, easy and accurate Gibbs sampling algorithm. The procedure is easy to implement and can be computed also when some priors in the...

estimation of agricultural sector demand and supply and identification of its determinants could lead to more efficient policies and planning of this section. in this study, aggregate demand and supply of agriculture sector for years of 1959-2007 by using of nonlinear restricted armax model are estimated. result indicates that aggregate agriculture demand is inelastic in short and long run. tho...

We introduce a new Bayesian nonparametric approach to identification of sparse dynamic linear systems. The impulse responses are modeled as Gaussian processes whose autocovariances encode the BIBO stability constraint, as defined by the recently introduced “Stable Spline kernel”. Sparse solutions are obtained by placing exponential hyperpriors on the scale factors of such kernels. Numerical exp...

This paper presents a mathematical model of an inventory system from the warehouse of goods Distribution Company using system identification approach. Considering items ordered from suppliers and items shipped to customers, as the inputs of the system and the stock level as the output system. In this paper, ARX model and ARMAX model are outlined and compared. The performances of each type of mo...