In this article we will present a detailed study of recursive extended least squares method (RELS) to identify without bias of the models (system + disturbance). The idea is simultaneously to identify the model of the system and the model of the disturbance to obtain asymptotically a white error of prediction, which guarantees an unbiased estimate of the parameters system. Then we choose an app...

In the mining sector, the barrier to obtain an efficient safety management system is the unavailability of future information regarding the accidents. This paper aims to use the auto-regressive integrated moving average (ARIMA) model, for the first time, to evaluate the underlying causes that affect the safety management system corresponding to the number of accidents and fatalities in the surf...

With the development of Internet and computer science, computer network is changing people’s lives. Meanwhile, Network traffic prediction model itself becomes more and more complex. It is an important research direction to quickly and accurately detect the intrusions or attacks. The performance efficiency of a network intrusion detection system is dominated by pattern matching algorithm. Howeve...

To investigate the variability in energy output from a network of photo-voltaic cells, solar radiation was recorded at ten sites every ten minutes in the Pentland Hills to the south of Edinburgh. We identify spatio-temporal auto-regressive moving average (STARMA) models as the most appropriate to address this problem. Although previously considered computationally prohibitive to work with, we s...

1. ABSTRACT In this paper we propose a model-based approach for multiresolution fusion of remotely sensed images. Given a high spatial resolution & low spectral resolution PAN (Panchromatic) image and a low spatial resolution & high spectral resolution MS (Multispectral) images of the same geographical area, the objective is to enhance the spatial resolution of the MS images to that of the PAN ...

The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimoneous representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help...

A two-phased method for prediction in spatialtemporal domains is proposed. After an ordinary regression model is trained on spatial data, a prediction is adjusted by incorporating autoregressive modeling of residuals in time. The prediction accuracy of the proposed method is evaluated on simulated agricultural data with a significant improvement of accuracy for both linear and non-linear regres...