Improving time series forecastingaccuracy is an important yet often difficult task.Both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. In this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

reservoir models with many grid blocks suffer from long run time; it is hence important to deliberatea method to remedy this drawback. usual upscaling methods are proved to fail to reproduce fine gridmodel behaviors in coarse grid models in well proximity. this is attributed to rapid pressure changesin the near-well region. standard permeability upscaling methods are limited to systems with lin...

introduction: time series models are generally categorized as a data-driven method or mathematically-based method. these models are known as one of the most important tools in modeling and forecasting of hydrological processes, which are used to design and scientific management of water resources projects. on the other hand, a better understanding of the river flow process is vital for appropri...

The activity of a neuronal network, characterized by action potentials (spikes), is constrained by the intrinsic properties of neurons and their interactions. When a neuronal network is submitted to external stimuli, the statistics of spikes changes, and it is difficult to disentangle the influence of the stimuli from the intrinsic dynamics. Using the formalism of Gibbs distributions, which are...

The object of this p~per is to present a general structure for mixed models which can be used to unify a large number of linear models often encountered in agricultural research work. Researchers often encounter special cases of these models under such diverse conditions that they fail to recognize the links to a common structure. ConSequently, in practice it is not unusual to find very special...