نتایج جستجو برای: layer neural network contains a structure including one input layer with 9 neurons
تعداد نتایج: 16146098 فیلتر نتایج به سال:
in this paper, a bidirectional associative memory (bam) neural network, which consists of two neurons in the x-layer and three neurons in the y-layer, with two time delays will be studied. we conclude that under some assumptions, hopf bifurcation occurs when the sum of two delays passes through a critical value. a numerical example is presented to support our theoretical results.
Organizations expose to financial risk that can lead to bankruptcy and loss of business is increased nowadays. This may leads to discontinuity in operations, increased legal fees, administrative costs and other indirect costs. Accordingly, the purpose of this study was to predict the financial crisis of Tehran Stock Exchange using neural network and genetic algorithm. This research is descripti...
Drought is one of the most destructive natural disasters in human societies that cause irreparable impacts on agriculture, environment, society and economics. So, awareness of occurrence of droughts can be effective in reducing losses. In this study, in order to modeling and forecasting drought severity in a 37 year time period (1971-2007) in 21 meteorological stations, located in the cold semi...
background and objectives: rheological characteristics of dough are important for achieving useful information about raw-material quality, dough behavior during mechanical handling, and textural characteristics of products. our purpose in the present research is to apply soft computation tools for predicting the rheological properties of dough out of simple measurable factors. materials and met...
Flood is one of the natural disaster phenomena and flood prediction is very important. The rainfall-runoff process and flood are physical phenomena that these analyses are difficult due to the influence of various parameters. There are different methods and models for these phenomena analysis. This study is carried out for rainfall-runoff process simulation using artificial neural network (ANN...
Risks assessment of forest project implementation in spatial density changes of forest under canopy vegetation using artificial neural network modeling approach Nowadays, environmental risk assessment has been defined as one of the effective in environmental planning and policy making. Considering the position and structure of vegetation on the forest floor, the main role of forest under ca...
Groundwater quality management is the most important issue in many arid and semi-arid countries, including Iran.Artificial neural network (ANN) has an extensive range of applications in water resources management. In this study,artificial neural network was developed using MATLAB R2013 software package, and Cl, EC, SO4 and NO3 qualitativeparameters were estimated and compared with the measured ...
runoff is one of the major components of calculating water resource processes and is the main issue in hydrology. many concept models are used to predict the amount of runoff, which in most cases depend on topographical and hydrological data. conventional models are not appropriate for areas in which there is little hydrological data. changes in runoff are nonlinear, meaning it is time & space ...
One of the natural disasters that occurs in abundance in Iran, due to the geological structure, morphological and seismic conditions, and damages the lives and property of people is a landslide. Roodbar Alamoot watershed in the east of Qazvin province is a mountainous region with a high potential for occurrence of landslides. Because of their active status, there is also a growing trend of...
Free swelling index (FSI) is an important parameter for cokeability and combustion of coals. In this research, the effects of chemical properties of coals on the coal free swelling index were studied by artificial neural network methods. The artificial neural networks (ANNs) method was used for 200 datasets to estimate the free swelling index value. In this investigation, ten input parameters ...
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