ozone concentration in metropolitan areas frequently exceed regulatory standards now. since it is harmful on human health and environment, its modeling and visualization are of vital important. modeling and prediction of ozone is required by urban managers to control and prevent its effects. in this paper, important parameters influencing the hourly ozone concentration are estimated by using da...

In this paper, a recurrent neural network for solving linear programming problems is presented that is simpler, intuitive and fast converging. To achieve optimality in accuracy and also in computational effort, an algorithm is presented. We investigate in this paper the MATLAB Simulink modeling and simulative verification of such a recurrent neural network. Modeling and simulative results subst...

ecological changes resulting from climate conditions can severely affect human societies especially in the area of economy and safety. climate catastrophes may cause social and economic tension. forecasting such changes accurately can help the government to control the disasters and to achieve possible benefits (such as water supply in flood). weather forecasting is the application of science a...

estimating of forest equipment productivity is an important aspect of managing cost in forestry, which leads to reduction of operations expenses. in other words, high capital cost in forest harvesting, is a good reason to argue forest engineering research and time modeling. this paper applied one of the artificial intelligence subsets, which are called artificial neural networks (anns), to pred...

accurate permeability estimation has always been a concern in determining flow units, assigning appropriate capillary pressure andrelative permeability curves to reservoir rock types, geological modeling, and dynamic simulation.acoustic method can be used as analternative and effective tool for permeability determination. in this study, a four-step approach is proposed for permeability estimati...

In this paper, we aim to achieve two specific objectives. The first one is to examine the applicability of wavelet neural network (WNN) technique in ore grade estimation, which is based on integration between wavelet theory and Artificial Neural Network (ANN). Different wavelets are applied as activation functions to estimate Cu grade of borehole data in the hypogene zone of porphyry ore deposi...

An exhaustive study on neural network language modeling (NNLM) is performed in this paper. Different architectures of basic neural network language models are described and examined. A number of different improvements over basic neural network language models, including importance sampling, word classes, caching and bidirectional recurrent neural network (BiRNN), are studied separately, and the...

In this paper, a novel methodology called a reference model approach to stability analysis of neural networks is proposed. The core of the new approach is to study a neural network model with reference to other related models, so that different modeling approaches can be combinatively used and powerfully cross-fertilized. Focused on two representative neural network modeling approaches (the neu...

The artificial neural networks (ANNs) have been applied to various hydrologic problems recently. This research demonstrates static neural approach by applying Multilayer perceptrons neural network and Radial basis function neural network to rainfall-runoff modeling for the upper area of Wardha River in India. The model is developed by processing online data over time using static modeling. Meth...