4.5 Article

On the approximation by neural networks with bounded number of neurons in hidden layers

Journal

JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS
Volume 417, Issue 2, Pages 963-969

Publisher

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jmaa.2014.03.092

Keywords

Neural network; MLP model; Sigmoidal function; Approximation; Superposition

Funding

  1. SOCAR Science Foundation of Azerbaijan [SOCAR EF 2013]

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In the current note, we show that a two hidden layer neural network with d inputs, d neurons in the first hidden layer, 2d+2 neurons in the second hidden layer and with a specifically constructed sigmoidal and infinitely differentiable activation function can approximate any continuous multivariate function with arbitrary accuracy. (C) 2014 Elsevier Inc. All rights reserved.

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