Density and Approximation by Using Feed Forward Artificial Neural Networks
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Abstract
I n this paper ,we 'viii consider the density questions associC;lted with the single hidden layer feed forward model. We proved that a FFNN with one hidden layer can uniformly approximate any continuous function in C(k)(where k is a compact set in R11 ) to any required accuracy.
However, if the set of basis function is dense then the ANN's can has al most one hidden layer. But if the set of basis function non-dense, then we need more hidden layers. Also, we have shown that there exist localized functions and that there is no theoretical lower bound on the degree of a pproximation common to all acti vation functions(contrary to the si tuation in the single hidden layer model).
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Density and Approximation by Using Feed Forward Artificial Neural Networks. (2017). Ibn AL-Haitham Journal For Pure and Applied Sciences, 20(1), 146-159. https://jih.uobaghdad.edu.iq/index.php/j/article/view/1335
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How to Cite
Density and Approximation by Using Feed Forward Artificial Neural Networks. (2017). Ibn AL-Haitham Journal For Pure and Applied Sciences, 20(1), 146-159. https://jih.uobaghdad.edu.iq/index.php/j/article/view/1335