this study aims at presenting a numerical model for predicting grout flow and penetration length into the jointed rock mass using universal distinct element code (udec). the numerical model is validated using practical data and analytical method for grouting process. input data for the modeling, including geomechanical parameters along with grout properties, were obtained from a case study. the...

in today’s business competitive world, decision makers of companies try to employ standard, efficient, theoretical and operational proven methods as a competitive advantage for making their critical strategic business decisions in order to survive in their industry. in this paper, a hybrid model based on fuzzy analytic hierarchy process (fahp) and artificial neural network (ann) is presented. t...

The paper deals with Data Envelopment Analysis (DEA) and Artificial Neural Network (ANN). We believe that solving for the DEA efficiency measure, simultaneously with neural network model, provides a promising rich approach to optimal solution. In this paper, a new neural network model is used to estimate the inefficiency of DMUs in large datasets.

Nowadays, firms apply the merger and acquisition strategy for gaining synergy, increasing the wealth of stockholders, economics of scales, enhancing efficiency, increasing the ability to research and develop, developing the firm and decreasing the risk. Developing an optimized model with the ability to identify the effective variables on the merger and acquisition process has a significant ...

The problem of synchronization in heterogeneous networks linear systems with nonlinear delayed diffusive coupling is considered. network presented new coordinates mean-field dynamics and errors. Thus the reduced to studying synchronization-error system stability. circle criterion for time-delay used derive stability conditions system. Obtained results are applied a neural mass model populations...

In this paper, we introduce a hybrid approach based on neural network and optimization teqnique to solve ordinary differential equation. In proposed model we use heyperbolic secont transformation function in hiden layer of neural network part and bfgs teqnique in optimization part. In comparison with existing similar neural networks proposed model provides solutions with high accuracy. Numerica...