Estimation of the nonlinear buckling capacity of thin walled shells is one of the most important aspects of structural mechanics. In this study the axial buckling load of 132 stiffened shells were numerically calculated. The applicability of artificial neural networks (ANN) in predicting the buckling capacity of vertically stiffened shells was studied. To this end feed forward (FF) multi-layer ...

in the present study the adiabatic temperature of gaseous fuels were calculated and the influence of effective parameters of flame temperature was discussed. firstly, a new computational program named ftc (flame temperature calculations) was prepared to calculate the adiabatic flame temperature and then the effect of initial temperatures of combustion air and fuel, excess air content and oxygen...

-There have been a number of methods presented by various researchers for traffic prediction, some of which involve modeling the problem of traffic prediction as a time series. It has been observed that Artificial Neural Networks (ANN) perform better than statistical methods for time series forecasting. The network performance and complexity varies with the choice of algorithm used. Back propag...

in recent years, various time series models have been proposed for financial markets forecasting. in each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. many researchers have compared different time series models together in order to determine more efficient ...

Feedforward neural networks have been theoretically proved to be able to approximate a nonlinear function to any degree of accuracy as long as enough nodes exist in the hidden layer(s) (Hornik et. al. 1989). However, when feedforward neural networks are applied to modeling physical systems in the real world, people care more about their prediction capabilities than accurate modeling abilities. ...

Electricity cannot be stored and needs huge amount of capital so producers and consumers pay special attention to predict electricity consumption. Besides, time-series data of the electricity market are chaotic and complicated. Nonlinear methods such as Neural Networks have shown better performance for predicting such kind of data. We also need to analyze other variables affecting electricity c...

In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...

This paper presents a new approach to the problem of multiclass classification. The proposed approach has the capability to provide an assessment of the uncertainty value associated with the results of the prediction. Two feed-forward backpropagation neural networks, each with multiple outputs, are used. One network is used to predict degrees of truth membership and another network is used to p...

Existing models based on artificial neural networks (ANNs) for sentence classification often do not incorporate the context in which sentences appear, and classify sentences individually. However, traditional sentence classification approaches have been shown to greatly benefit from jointly classifying subsequent sentences, such as with conditional random fields. In this work, we present an ANN...