Finding the best structure of ANN to minimize errors, processing and search time is one main objectives in AI field. In order achieve prediction with a high degree accuracy short time, an enhanced PSO-based selection technique determine optimal configuration for artificial neural network has been proposed this paper. To design maximize prediction, it necessary identify hyperparameter values pre...

This paper proposes a two-step approach for characterizing the reservoir properties of the world’s largest non-associated gas reservoir. This approach integrates geological and petrophysical data and compares them with the field performance analysis to achieve a practical electrofacies clustering. Porosity and permeability prediction is done on the basis of linear functions, succeeding the elec...

In this paper we solve a wide rang of Semidefinite Programming (SDP) Problem by using Recurrent Neural Networks (RNNs). SDP is an important numerical tool for analysis and synthesis in systems and control theory. First we reformulate the problem to a linear programming problem, second we reformulate it to a first order system of ordinary differential equations. Then a recurrent neural network...

the method of artificial neural network is used as a suitable tool for intelligent interpretation of gravity data in this paper. we have designed a hopfield neural network to estimate the gravity source depth. the designed network was tested by both synthetic and real data. as real data, this artificial neural network was used to estimate the depth of a qanat (an underground channel) located at...

the aim followed in this study was to compare the performance of multiple regression vs neural network models to predict the activity of antioxidant enzymes super oxide dismutase (sod), cat alase (cat), ascorbate pero xidase (apx) and peroxidase (pox) in the shoots of wheat (triticum aestivum), alvand cultivar in a soil polluted with cadmium. the treatments consisted of four levels of cadmium (...