ystem identification is mainly the process of improving a mathematical modeling of a physical system using experimental data. In this paper, a new hybrid wavelet neural network is proposed for the system identification purposes. The Gravitational Search Algorithm (GSA) is a new evolutionary algorithm which recently introduced and has a good performance in different optimization problems. The GS...

In this research, a hybrid wavelet-artificial neural network (WANN) and a geostatistical method were proposed for spatiotemporal prediction of the groundwater level (GWL) for one month ahead. For this purpose, monthly observed time series of GWL were collected from September 2005 to April 2014 in 10 piezometers around Mashhad City in the Northeast of Iran. In temporal forecasting, an artificial...

the goal of this research is to predict total stock market index of tehran stock exchange, using the compound method of arima and neural network in order for the active participations of finance market as well as macro decision makers to be able to predict trend of the market. first, the series of price index was decomposed by wavelet transform, then the smooth's series  predicted by using...

Predicting Tra c flow in the busiest cities has become a popular research area in the past decades. The rapid development of intelligent tra c management system attracts the software industry to come up with e cient tools for tra c prediction over the roads. In this study, Discrete Wavelet Transformation (DWT) is employed with Artificial Neural Network (ANN) to forecast the tra c flow over the ...

In this study, a Mexican Hat Wavelet scalogram neural genetic network approach is proposed for signal classification. The Wavelet Scalogram network uses a Levenberg-Marquardt multilayer feedforward neural network-genetic algorithm hybrid structure, and its input layer constitutes the feature extraction part, whereas the hidden layer and output layer constitute the signal classification part. Fr...

This paper proposes a hybrid modelling approach for forecasting returns and volatilities of the stock market. The model, called ARFIMA-WLLWNN integrates advantages ARFIMA wavelet decomposition technique (namely, discrete MODWT with Daubechies least asymmetric filter) artificial neural network LLWNN network). model develops through two-phase approach. In phase one, improves accuracy network, res...

in recent decades, the developments of artificial intelligence to predict hydrologic models have been widely used. in this study, the ability of artificial neural network(ann) models for modeling and predict the biological oxygen demand (bod) is located on the karun river in west iran were evaluated. to improve the simulation results, wavelet analysis was used as a hybrid model. bod index month...