Neural networks have become standard tools in modeling and classiication. In this paper we discuss some advancements in controling the model complexity in neural network modeling, and give some practical advise to users of these techniques.

Ore grade estimation is a key aspect in the evaluation of a mineral deposit. In this paper an alternative approach to currently applied methods of ore grade estimation is presented. This alternative approach involves a modular neural network system integrated in a state of the art 3D resource modelling package. The need for a new method of ore grade estimation comes from the difficulties in app...

Since the 1970s, most airlines have incorporated computerized support for managing disruptions during flight schedule execution. However, existing platforms airline disruption management (ADM) employ monolithic system design methods that rely on creation of specific rules and requirements through explicit optimization routines, before a meets specifications is designed. Thus, current ADM are un...

geological facies interpretation is essential for reservoir studying. the method of classification and identification seismic traces is a powerful approach for geological facies classification and distinction. use of neural networks as classifiers is increasing in different sciences like seismic. they are computer efficient and ideal for patterns identification. they can simply learn new algori...

We compare two approaches for a Markovian model in flexible manufacturing systems (FMSs) using Monte Carlo simulation. The model which is a development of Fazlollahtabar and Saidi-Mehrabad (2013), considers two features of automated flexible manufacturing systems equipped with automated guided vehicle (AGV) namely, the reliability of machines and the reliability of AGVs in a multiple AGV jobsho...

Two main objectives have been considered in this paper: providing a good model to predict the critical temperature and pressure of binary hydrocarbon mixtures, and comparing the efficiency of the artificial neural network algorithms and the support vector regression as two commonly used soft computing methods. In order to have a fair comparison and to achieve the highest efficiency, a comprehen...