Forecasting a time series became one of the most challenging tasks to variety of data sets. The existence of large number of parameters to be estimated and the effect of uncertainty and outliers in the measurements makes the time series modeling too complicated. Recently, Artificial Neural Network (ANN) became quite successful tool to handle time series modeling problem. This paper provides a s...

In this paper, one of the most important criterion in public services quality named availability is evaluated by using artificial neural network (ANN). In addition, the availability values are predicted for future periods by using exponential weighted moving average (EWMA) scheme and some time series models (TSM) including autoregressive (AR), moving average (MA) and autoregressive moving avera...

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. 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 coefficients of...

Rainfall-Runoff models are mostly empirical in nature demanding the knowledge of a large number of catchment parameters. On the contrary Artificial Neural Networks (ANN) can be deployed in cases where the available data is limited. The present work involves the development of an ANN model using Backward Propagation algorithm. The hydrologic variables used were Rainfall, Soil Moisture, Evaporati...

Artificial Neural Networks (ANN) have traditionally enjoyed considerable attention in process control applications. Thus, the paper is focused on real system control design using neural networks. The point is to show whether neural networks bring better performances to nonlinear process control or not. Artificial Neural Network is nowadays a popular methodology with lots of practical and indust...

Artificial neural networks (ANN), as computational models inspired by biological neural networks, have become a standard tool in computer science for the classification of various digital patterns. They are used primarily in applications dealing with image processing, speech recognition, time series prediction, robotics and others [1]. Traditional artificial neural networks use real numbers for...

runoff estimation is one of the main challenges encountered in water and watershed management. spatial and temporal changes of factors which influence runoff due to het-erogeneity of the basins explain the complicacy of relations. artificial neural network (ann) is one of the intelligence techniques which is flexible and doesn’t call for any much physically complex processes. these networks can...

background: forecasting of air pollutants has become a popular topic of environmental research today. for this purpose, the artificial neural network (aan) technique is widely used as a reliable method for forecasting air pollutants in urban areas. on the other hand, the evolutionary polynomial regression (epr) model has recently been used as a forecasting tool in some environmental issues. in ...