Modeling of stream flow–suspended sediment relationship is one of the most studied topics in hydrology due to itsessential application to water resources management. Recently, artificial intelligence has gained much popularity owing toits application in calibrating the nonlinear relationships inherent in the stream flow–suspended sediment relationship. Thisstudy made us of adaptive neuro-fuzzy ...

Over the past few years, neural networks have re-emerged as powerful machine-learning models, yielding state-of-the-art results in fields such as image recognition and speech processing. More recently, neural network models started to be applied also to textual natural language signals, again with very promising results. This tutorial surveys neural network models from the perspective of natura...

ABSTRACT The contribution is aimed at predictive control of nonlinear processes with the help of artificial neural networks as the predictor. Since this methodology is relatively wide, paper only concentrates on the prediction via artificial neural networks. Special attention is paid to the usage of offline-learnt predictor based on multilayer feed forward neural network. The proposed method is...

In this paper, we use parametric form of fuzzy number, then feed-forward neural network is presented for obtaining approximate solution for fuzzy Fredholm integro-differential equation of the second kind. This paper presents a method based on neural networks and Newton-Cotes methods with positive coefficient. The ability of neural networks in function approximation is our main objective. The pr...

forecasting of macroeconomic variables has specific importance in economic topics. indeed, different models are invented to forecast variables to help economic policy makers in adopting appropriate monetary and fiscal policies. in this paper, the performance of integrated model of input-output (io) and neural network is investigated in forecasting final demand and total production and the resul...

Artificial neural networks can be trained with relatively low-precision floating-point and fixed-point arithmetic, using between one and 16 bits. Previous works have focused on relatively wide-but-shallow, feed-forward networks. We introduce a quantization scheme that is compatible with training very deep neural networks. Quantizing the network activations in the middle of each batch-normalizat...

Feed-Forward Neural Networks are nowadays a standard tool in the toolbox of high energy physicists. This report summarizes the elds of application in ooine analysis, and discusses some open problems.

in the present study iran’s rice imports trend is forecasted, using artificial neural networks and econometric methods, during 2009 to 2013, and their results are compared. the results showed that feet forward neural network leading with less forecast error and had better performance in comparison to econometric techniques and also, other methods of neural networks, such as recurrent networks a...

The feed-forward multi-layer neural networks have significant importance in speech recognition. A new parallel-training tool TNet was designed and optimized for multiprocessor computers. The training acceleration rates are reported on a phoneme-state classification task.

This paper presents a defect segmentation work for bi-colored apple fruits performed by several artificial neural networks. Pixel-wise classification approach is employed to realize segmentation. Quantitative and qualitative evaluations showed that competitive networks were more erroneous while feed-forward and recurrent networks tested were more accurate in segmenting apple defects.