نتایج جستجو برای: neural network approximation

تعداد نتایج: 1008466  

سادات فیض نیا, , محمد مهدوی, , کارو لوکس, , علی رضایی, , محمد حسین مهدیان, ,

The model in this research was created based on the Artificial Neural Network (ANN) and calibrated in the Sefid-rood dam basin (excluding Khazar zone). This research was done by gathering and selecting peak flows of hydrographs from 12 sub basins, the concentration time of which was equal to or less than 24 hours and was caused only by rainfall. From all the selected sub basins, totally 661 hyd...

Journal: :CoRR 2016
Ramon Oliveira Pedro Tabacof Eduardo Valle

We evaluate the uncertainty quality in neural networks using anomaly detection. We extract uncertainty measures (e.g. entropy) from the predictions of candidate models, use those measures as features for an anomaly detector, and gauge how well the detector differentiates known from unknown classes. We assign higher uncertainty quality to candidate models that lead to better detectors. We also p...

Journal: :Foundations of Computational Mathematics 2022

Abstract In certain polytopal domains $$\varOmega $$ ? , in space dimension $$d=2,3$$ d = 2 , 3 we prove exponential expressivity with stable ReLU Neural Networks (ReLU NNs) $$H^1(\varOmega )$$ <mml:msu...

Journal: :Computers & mathematics with applications 2022

In this paper, we introduce adaptive network enhancement (ANE) method for the best least-squares approximation using two-layer ReLU neural networks (NNs). For a given function f(x), ANE generates NN and numerical integration mesh such that accuracy is within prescribed tolerance. The provides natural process obtaining good initialization which crucial training nonlinear optimization problems. N...

Journal: :IEEJ Transactions on Electronics, Information and Systems 2000

2006
Ying Li Zhidong Deng

Inspired by the theory of multiresolution analysis (MRA) of wavelets and artificial neural networks, a multiresolution neural network (MRNN) for approximating arbitrary nonlinear functions is proposed in this paper. MRNN consists of a scaling function neural network (SNN) and a set of sub-wavelet neural networks, in which each sub-neural network can capture the specific approximation behavior (...

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