computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. nowadays, despite the numerous time series forecasting models propos...

the stochastic models for ship heave motion in irregular sea waves based on white and colored noises are examined. for this purpose the deterministic model transfers to stochastic models by adding different noise terms in force and then these models are solved analytically and numerically. finally an investigation is undertaken to examine the parameter estimation problem of second order stochas...

Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm.   In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact,...

in this paper, controlling the gyros with linear plus cubic damping is considered. hence, an adaptive control algorithm is proposed to overcome the uncertainties in model parameters and the chaotic behavior of gyroscopes. it is noted that for general applications dynamic model coefficient of gyros is not precisely determined, on the other hand, with respect to a particular coefficient gyroscope...

This is an expository paper that reviews recent developments on optimal estimation of structured high-dimensional covariance and precision matrices. Minimax rates of convergence for estimating several classes of structured covariance and precision matrices, including bandable, Toeplitz, and sparse covariance matrices as well as sparse precision matrices, are given under the spectral norm loss. ...