Eman  Bhaya, Omar  Al-sammak, 
    Approximation by Regular Neural Networks in Terms of Dunkl Transform,
    Research Journal of Applied Sciences,
    Volume 11,Issue 10,
    2016,
    Pages 933-941,
    ISSN 1815-932x,
    rjasci.2016.933.941,
    (https://makhillpublications.co/view-article.php?doi=rjasci.2016.933.941)
    Abstract: Dunkl operator here we introduce a modified version of and use it to prove a theorem shows that functionals and rth order modulus of smoothness in K-theorem shows thatare equivalent. We use this equivalence to introduce p&lt;1 spaces for L<sub>p</sub> (K) essential degree of approximation using regular neural networks p and how a multivariate function in spaces for can be approximated using a p&lt;1 spaces for L<sub>p</sub> (K) multivariate p function in forward regular neural network. So, we can have the essential approximation using regular FFN. P&lt;1 spaces for L<sub>p</sub> (K) ability of a multivariate function in spaces for using regular FFN.
    Keywords: Neural network approximation;saturation problem;spaces;direct inequality