The main contribution of the present paper is to train efficient neural networks for seismic design of double layer grids subject to multiple-earthquake loading. As the seismic analysis and design of such large scale structures require high computational efforts, employing neural network techniques substantially decreases the computational burden. Square-on-square double layer grids with the va...

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...

A convolutional neural network can be constructed using numerical methods for solving dynamical systems, since the forward pass of regarded as a trajectory system. However, existing models based on solvers cannot avoid iterations implicit methods, which makes inefficient at inference time. In this paper, we reinterpret pre-activation Residual Networks (ResNets) and their variants from systems v...

In this study, an integrated neural network control architecture for nonlinear dynamic systems is presented. Most of the recent emphasis in the neural network control field has no error feedback as the control input, which rises the lack of adaptation problem. The integrated architecture in this paper combines feed forward control and error feedback adaptive control using neural networks. The p...

Meat, as an important source of protein, is one of the main parts of many people’s diet. Due toeconomic interests and thereupon adulteration, there are special concerns on its accurate labeling.In this study Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometrictechniques (principal component analysis (PCA), artificial neural networks (ANNs), and partial<...