نتایج جستجو برای: general regression neural network
تعداد نتایج: 1769952 فیلتر نتایج به سال:
in this paper attempts have been done to create a mortar with relatively high uniaxial compressive strength (ucs), easy casting, high flexibility, instant hardening, low cost and easy availability. the main use of this material is to physically model the mechanical behavior of jointed rock-like blocks. the effect of four parameters such as joint roughness coefficient (jrc), bridge length (l), b...
ardekoul fault zone which is along nnw-sse is located in eastern iran and northern part of the sistan subzone. dextral strike-slip fault zone of ardekuol is made of six main segments including korizan (28 km), bohn abad (8.8 km), abiz (32 km), gazkoon (20 km), moein abad (30.4 km), ghal maran (12 km), and two minor segments including olang morgh (12.8 km) and seh pestan (9.6 km). the studied r...
Abstract. General Regression Neural Network (GRNN) is a nonparametric method of developing the concept an artificial neural network. The GRNN operation based on estimated expected output value determined by input set. One characteristics that number neurons in pattern layer will increase with amount training data. This problem can be solved K-means. K-means this study aims to obtain various gro...
In this note we present and discuss results of experiments comparing the performance of six neural network architectures (back propagation, recurrent network with dampened feedback, network with multiple hidden layers each with a different activation function, jump connection networks, probabilistic neural networks and general regression neural networks) applied to a simplified multi-font recog...
Introduction: Protein kinase causes many diseases, including cancer; therefore, inhibiting them plays an important role in the treatment of many diseases. Traditional discovery inhibitors of this enzyme is a time-consuming and costly process. Finding a reliable computer-aided drug discovery tools which can detect the inhibitors will reduce the cost. In this study, it is attempted to separate ki...
Abstract Twin neural network regression (TNNR) is trained to predict differences between the target values of two different data points rather than targets themselves. By ensembling predicted an unseen point and all training points, it possible obtain a very accurate prediction for original problem. Since any loop should sum zero, loops can be supplied data, even if themselves within are unlabe...
Background and Objectives: Diabetic patients are always at risk of hypertension. In this paper, the main goal was to design a native cost sensitive model for the diagnosis of hypertension among diabetics considering the prior probabilities. Methods: In this paper, we tried to design a cost sensitive model for the diagnosis of hypertension in diabetic patients, considering the distribution of...
Accurate flood forecasting is a vital need to reduce its risks. Due to the complicated structure of flood and river flow, it is somehow difficult to solve this problem. Artificial neural networks, such as frequent neural networks, offer good performance in time series data. In recent years, the use of Long Short Term Memory networks hase attracted much attention due to the faults of frequent ne...
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