The objective of this paper is to develop a novel wind speed forecasting technique, which produces more accurate prediction. The Wavelet Transform (WT) along with the Auto Regressive Moving Average (ARMA) is chosen to form a hybrid whose combination is expected to give minimum Mean Absolute Prediction Error (MAPE). A simulation study has been conducted by comparing the forecasting results using...

The use of digital predistortion for linearizing a millimeter-wave power amplifier (PA) is investigated. A PA operating at 38 GHz is designed using an accurate non-quasi-static transistor model, taking into account both shortand long-term memory effects. A realistic test signal is then used for the identification of a nonlinear auto-regressive moving average (NARMA) behavioral model of the PA. ...

This paper deals with the approximation of a given frequency response by a low-order linear ARMA filter (Auto-Regressive Moving Average). The aim of this work is the audio synthesis, then to improve the perceptual quality, a criterion based on human listening is defined and minimized. Two complementary approaches are proposed here for solving this non-linear and non-convex problem: first, a wei...

In this paper, the performance of different predictive vector quantization (PVQ) structures is studied and compared for different degrees of channel noise. Predictive quantization schemes with an auto-regressive (AR) decoder structure are compared with schemes that employ a moving average (MA) decoder. For noisy channels MA prediction performs better than AR. It is shown here that a combination...

We review the concepts of local and global invertibility for a nonlinear auto-regressive moving-average (NLARMA) model. Under very general conditions, a local invertibility analysis of a NLARMA model admits the generic dichotomy that the innovation reconstruction errors either diminish geometrically fast or grow geometrically fast. We derive a simple sufficient condition for a NLARMA model to b...

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...

The paper considers the development of a new type of artificial neural network and its applicability to non-linear system identification. This is the functional-link neural network with internal dynamic elements. The net consists of a single layer where the nonlinearity is firstly introduced by enhancing the input pattern with a functional expansion. The internal dynamic elements are auto-regre...

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...