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Universal approximation theorem

Known as: Cybenko Theorem, Universal approximator 
In the mathematical theory of artificial neural networks, the universal approximation theorem states that a feed-forward network with a single hidden… Expand
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Papers overview

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Highly Cited
2016
Highly Cited
2016
Deep learning takes advantage of large datasets and computationally efficient training algorithms to outperform other approaches… Expand
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Highly Cited
2016
Highly Cited
2016
With randomly generated weights between input and hidden layers, a random vector functional link network is a universal… Expand
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2016
2016
In this article, a brain-inspired winner-take-all emotional neural network (WTAENN) architecture is proposed and then the… Expand
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Highly Cited
2013
Highly Cited
2013
We consider the problem of designing models to leverage a recently introduced approximate model averaging technique called… Expand
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Highly Cited
2008
Highly Cited
2008
Recently an incremental algorithm referred to as incremental extreme learning machine (I-ELM) was proposed by Huang et al. [G.-B… Expand
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Highly Cited
2003
Highly Cited
2003
We study how closely the optimal Bayes error rate can be approximately reached using a classification algorithm that computes a… Expand
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Highly Cited
2000
Highly Cited
2000
Proposes a recurrent fuzzy neural network (RFNN) structure for identifying and controlling nonlinear dynamic systems. The RFNN is… Expand
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Highly Cited
2000
Highly Cited
2000
This paper deals with the robust adaptive control of a class of nonlinear systems in the presence of parametric uncertainties and… Expand
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Highly Cited
1994
Highly Cited
1994
  • P. Reignier
  • Robotics Auton. Syst.
  • 1994
  • Corpus ID: 205122216
Abstract This paper is concerned with the problem of reactive navigation for a mobile robot in an unknown clustered environment… Expand
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Highly Cited
1990
Highly Cited
1990
A neural network with a single layer of hidden units of gaussian type is proved to be a universal approximator for real-valued… Expand
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