information on nitrate in groundwater resources requires periodic measurements are accurate. despite the measure in some areas due to sensitive social and health community are not reported. therefore, be informed of the status of each area of water quality, modeling is essential. the purpose of this study was the application of artificial neural network method for estimating nitrate and compare...

results survival probabilities at different times were determined using the cox proportional hazards and a neural network with three nodes in the hidden layer; the ratios of standard errors with these two methods to the kaplan-meier method were 1.1593 and 1.0071, respectively, revealed a significant difference between cox and kaplan-meier (p < 0.05) and no significant difference between cox and...

A competitive neural network model and a genetic algorithm are used to improve the initialization and construction phase of a parallel insertion heuristic for the vehicle routing problem with time windows. The neural network identifies seed customers that are suitably distributed over the entire geographic area during the initialization phase, and the genetic algorithm finds good parameter sett...

NEURAL NETWORK REGRESSION Herbert K. H. Lee, Duke University Box 90251, Durham, NC 27708, herbie stat.duke.edu

This paper proposes an optimization strategy which is based on neural networks and genetic algorithms to calculate the optimal values of gas injection rate and oil rate for oil production system. Two cases are analyzed: a) A single well production system and b) A production system composed by two gaslifted wells. For both cases an objective function is maximized to reduce production cost. The p...

One of reasons that researchers in recent years have tried to produce ultrafine grained materials is producing lightweight components with high strength and reliability. There are disparate methods for production of ultra-fine grain materials,one of which is severe plastic deformation method. Severe plastic deformation method comprises different processes, one of which is Parallel tubular chann...

In this paper we propose and implement a fusion model by combining the Hidden Markov Model (HMM), Artificial Neural Networks (ANN) and Genetic Algorithms (GA) to forecast financial market behaviour. The developed tool can be used for in depth analysis of the stock market. Using ANN, the daily stock prices are transformed to independent sets of values that become input to HMM. We draw on GA to o...

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 ...