applying nonlinear models to estimation and forecasting economic models are now becoming more common, thanks to advances in computing technology. artificial neural networks (ann) models, which are nonlinear local optimizer models, have proven successful in forecasting economic variables. most ann models applied in economics use the gradient descent method as their learning algorithm. however, t...

hammerstein-wiener (hw) models are capable in describing nonlinear dynamic systems. these models are nonlinear and have been widely used in a wide range of sciences due to their simplicity and having a physically-based concept. in this research, for the first time in hydrology and water resources management, three different structures of these models using daily temperature and precipitation da...

In order to characterize the behavior of nonlinear dynamic systems many different approaches have been proposed in recent years. One of the best black-box models employed to deal with system nonlinearities is the combination of artificial neural network (ANN) and fuzzy logic system (FLS), which is known as neuro-fuzzy system. However, the gradient-based nature of this combination causes some de...

A new method based on principal component analysis (PCA) and artificial neural networks (ANN) is proposed for fault diagnosis of gearboxes. Firstly the six different base wavelets are considered, in which three are from real valued and other three from complex valued. Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared...

in this study, the artificial neural networks (anns) and regression models were used to downscale the simulated outputs of the general circulation models (gcms). the simulated precipitation for 25.18 º n to 34.51 º n and 45 º e to 60 º e, geopotential height at 850 mb and zonal wind at 200 mb for 12.56° n to 43.25° n and 19.68° e to 61.87° e data sets as the predictors were extracted from echam...

in recent years, wind energy has a remarkable growth in the world, but one of the important problems of power generated from wind is its uncertainty and corresponding power. for solving this problem, some approaches have been presented. recently, the artificial neural networks (ann) as a heuristic method has more applications for this propose. in this paper, short-term (1 hour) and mid-term (24...

In this paper, a fault diagnosis system based on discrete wavelet transform (DWT) and artificial neural networks (ANNs) is designed to diagnose different types of fault in gears and bearings. DWT is an advanced signal-processing technique for fault detection and identification. Five features of wavelet transform RMS, crest factor, kurtosis, standard deviation and skewness of discrete wavelet co...