نتایج جستجو برای: back neural network ffnn
تعداد نتایج: 971694 فیلتر نتایج به سال:
breathomics is the metabolomics study of exhaled air. it is a powerful emerging metabolomics research field that mainly focuses onhealth-related volatile organic compounds (vocs). since the quantity of these compounds varies with health status, breathomics assuresto deliver noninvasive diagnostic tools. thus, the main aim of breathomics is to discover patterns of vocs related to abnormal metabo...
In this work, adaptive learning of a monitored real-time stochastic phenomenon over an operational LTE broadband radio network interface is proposed using cascade forward neural (CFNN) model. The optimal architecture the model has been implemented computationally in input and hidden units by means incremental search process. Particularly, we have applied adaptive-based cascaded for realistic pr...
The prediction of the ultimate bearing capacity of the pile under axial load is one of the important issues for many researches in the field of geotechnical engineering. In recent years, the use of computational intelligence techniques such as different methods of artificial neural network has been developed in terms of physical and numerical modeling aspects. In this study, a database of 100 p...
In a daily power market, price and load forecasting is the most important signal for the market participants. In this paper, an accurate feed-forward neural network model with a genetic optimization levenberg-marquardt back propagation (LMBP) training algorithm is employed for short-term nodal congestion price forecasting in different zones of a large-scale power market. The use of genetic algo...
The main contribution of the present paper is to train efficient neural networks for seismic design of double layer grids subject to multiple-earthquake loading. As the seismic analysis and design of such large scale structures require high computational efforts, employing neural network techniques substantially decreases the computational burden. Square-on-square double layer grids with the va...
in this paper, i develop three forecasting models: namely structural, times series, and artificial neural networks; to forecast iranian inflation rates. the structural model uses aggregate demand and aggregate supply approach, the time series model is based on the standard arlma technique, and the artificial neural network applies multi-layer back propagation model the latter, which is rooted i...
background: clinically frank thyroid nodules are common and believed to be present in 4% to 10% of the adult population in the united states. in the current literature, fine needle aspiration biopsies are considered to be the milestone of a model which helps the physician decide whether a certain thyroid nodule needs a surgical approach or not. a considerable fact is that sensitivity and specif...
A system that segments and labels tabla strokes from real performances is described. Performance is evaluated on a large database taken from three performers under different recording conditions, containing a total of 16,834 strokes. The current work extends previous work by Gillet and Richard (2003) on categorizing tabla strokes, by using a larger, more diverse database that includes their dat...
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 any climatic condit...
Monitoring system for induction motor is widely developed to detect the incipient fault. Such system is desirable to detect the fault at the running condition to avoid the motor stop running suddenly. In this paper, a new method for detection system is proposed that emphasizes the fault occurrences as temporary short circuit in induction motor winding. The investigation of fault detection is fo...
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