Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fourier Neural Networks for Function Approximation   The success of Neural networks in providing miraculous results when applied
to a wide variety of tasks is astonishing. Insight in the working can be
obtained by studying the universal approximation property of neural networks.
It is proved extensively that neural networks are universal approximators.
Further it is proved that deep Neural networks are better approximators. It is
specifically proved that for a narrow neural network to approximate a function
which is otherwise implemented by a deep Neural network, the network take
exponentially large number of neurons. In this work, we have implemented
existing methodologies for a variety of synthetic functions and identified
their deficiencies. Further, we examined that Fourier neural network is able to
perform fairly good with only two layers in the neural network. A modified
Fourier Neural network which has sinusoidal activation and two hidden layer is
proposed and the results are tabulated.

19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph