A non-linear Auto-Regressive Exogenous-input model (NARXM) river flow forecasting output-updating procedure is presented. This updating procedure is based on the structure of a multi-layer neural network. The NARXM-neural network updating procedure is tested using the daily discharge forecasts of the soil moisture accounting and routing (SMAR) conceptual model operating on five catchments havin...

We consider linear time-invariant networks with unknown interaction topology where only a subset of the nodes, termed manifest, can be directly controlled and observed. The remaining nodes are termed latent and their number is also unknown. Our goal is to identify the transfer function of the manifest subnetwork and determine whether interactions between manifest nodes are direct or mediated by...

foreign trade of any country is considered as one of the major economic sectors of that society as far as some refer to trade as the “engine of growth”. foreign exchange rate and its volatilities is one of the effective factors determining foreign trade. the present paper deals with the impact of real exchange rate uncertainty on imports in iran during 1979-2009. in this respect, first, among v...

Speech synthesizers based on paramedic methods, still have not achieved the expected naturalness. This is due to less consideration on linear time variant nature between the neighbor phonemes. This paper presents a study to model the phoneme transitions between neighbor phonemes with lesser number of parameters using Auto Regressive Moving Average (ARMA) model, where Steiglitz-McBride algorithm...

We study the problem of learning the dependency graph between random processes in a Vector Autoregressive (VAR) model from samples when a subset of the variables are latent. We show that the dependencies among the observed processes can be identified successfully under some conditions on the VAR model. Moreover, we can recover the length of all directed paths between any two observed processes ...