Systems Analysis Modelling Simulation
Neural networks for event extraction from time series: a back propagation algorithm approach
Future Generation Computer Systems
Higher-order statistics based blind estimation of non-Gaussian bidimensional moving average models
Signal Processing - Fractional calculus applications in signals and systems
Neural networks for event extraction from time series: a back propagation algorithm approach
Future Generation Computer Systems
| Hi-index | 35.68 |
A general (possibly nonminimum phase and/or asymmetric noncausal) two-dimensional (2-D) moving average (MA) model driven by a zero-mean i.i.d. 2-D sequence is considered. The input sequence is not observed. The signal observations may be noisy. We consider the problems of model order determination and model parameter estimation using the higher order (third- or fourth-order, for example) cumulants of the 2-D signal. Second-order statistics of the data can consistently identify only a smaller class of MA models. The proposed approaches are illustrated via computer simulations