Decision Neural Network is a new approach for solving multi-objective decision-making problems based on artificial neural networks. Using inaccurate evaluation data, network training has improved and the number of educational data sets has decreased. The available training method is based on the gradient decent method (BP). One of its limitations is related to its convergence speed. Therefore,...

In this work, we model and analyze the problem of stable and efficient pricing for interdomain traffic routing in the future Internet. We consider a general network topology with multiple sources and sinks of traffic, organized into separate domains managed by Internet Service Providers (ISPs) solely interested in maximizing their own profit. In this framework, we prove that there exists a pric...

evaluate the performance of companies on the stock exchange using non-parametric methods is very important. dea and dea-r with the strategies for piecewise linear frontier production function and use of available data, assess the stock company. in this study, using a neural network algorithm dea and dea-r is suggested to classify the first companies in the stock exchange; secondly, using the co...

As examples of a state of agents, this paper will present a comparative analysis of the evolution of two community mental health networks that both have similar contracts from the State of Arizona and operate under the same set of rules. One of these is governed by a for-profit firm that both produces services directly and buys them from a network of nonprofit agencies. The other is governed by...

efficiency of the existing protected areas network in conservation of biodiversity is one of the most important challenges of protected areas custodians. despite there are numerous articles on evaluating the efficiency of the protected areas for conserving the habitat and species on national and international scales, in iran this subject has not been studied yet. the aim of the present study is...