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...

An Artificial Neural Network (ANN) was used to analyse the capillary rise in porous media. Wetting experiments were performed with fifteen liquids and fifteen different powders. The liquids covered a wide range  of  surface  tension ( 15.45-71.99  mJ/m2 )  and  viscosity (0.25-21 mPa.s). The powders also provided an acceptable range of particle size (0.012-45 μm) and surface free...

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...

in this study, a low-cost, rapid and qualitative evaluation procedure is presented using dynamic pattern recognition analysis to assess liquefaction potential which is useful in the planning, zoning, general hazard assessment, and delineation of areas, dynamic pattern recognition using neural networks is generally considered to be an effective tool for assessing of hazard potential on the basis...

This paper presents an artificial neural network (ANN) based scheme for fault identification in power transformer protection. The proposed scheme is featured by the application of ANN to identifying system patterns, the unique choice of harmonics of positive sequence differential currents as ANN inputs, the effective handling of current transformer (CT) saturation with an ANN based approach, an...

many of the meteorological variables such as precipitation, strongly depend on the large scale atmospheric and ocean surface circulations.in the current study, the effect of climatic signals on the average monthly rainfall of the adjacent stations of sheshdeh and gharebolagh area was investigated during the statistical period twenty five years from 1985 to 2009. the regression and neural networ...

three artificial neural networks (ann) models; general regression neural network (grnn), redial basis function (rbf) and three layer multiple perceptron network were carried out to evaluate the prediction of the apparent metabolizable energy (ame) of wheat and corn from its chemical composition in broiler. input variables included: gross energy (ge), crude protein (cp), crude fiber (cf), ether ...

the main purpose of this paper is prediction of tse corporate financial bankruptcy using artificial neural networks. the mean values of key ratios reported in past bankruptcy studies were selected for neural network inputs (working capital to total assets, net income to total assets, total debt to total assets, current assets to current liabilities, quick assets to current liabilities). the neu...