نتایج جستجو برای: network models
تعداد نتایج: 1491590 فیلتر نتایج به سال:
Drought Forecasting in Khash City by Using Neural Network Model Hossein Negaresh Associate Professor of Geography and Environmental PlanningFaculty, University of Sistan & Baluchestan Mohsen Armesh Holding Master Degree in climatology in Environmental Planning Extended Abstract 1- Introduction Drought is condition of lack of rainfall and increase in temperature occurring in...
allocation and removing of excess water from the irrigation and drainage network is one of the most important activities in the management of these networks. side weir is one of the most common structures for this purpose. study on the flow hydraulic characteristics of this structure included two parts, defining the water surface profiles and estimating the discharge coefficient. to estimate th...
in this study the wavelet neural network (wnn) and artificial neural network (ann) were used to simulate barley breakage percentage in combine harvester. the models have been trained using the same data conditions. air temperature, thresher cylinder speed, distance between thresher cylinder and concave (back and forth) and the percentage of barely moisture were as the input variables. the resul...
production of highly viscous tar sand bitumen using steam assisted gravity drainage (sagd) with a pair of horizontal wells has advantages over conventional steam flooding. this paper explores the use of artificial neural networks (anns) as an alternative to the traditional sagd simulation approach. feed forward, multi-layered neural network meta-models are trained through the back-error-propaga...
This study is an applied study conducted to evaluate and compare the efficiency of Iran's regional power companies using conventional and network Data Envelopment Analysis methods. Iran's regional power companies use two- phased process to transmit power. Using applied approach, the performance and efficiency of these companies were measured with network and conventional methods, and they were ...
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