@article{MAKHILLRJAS201611109868,
    title = {Approximation by Regular Neural Networks in Terms of Dunkl Transform},
    journal = {Research Journal of Applied Sciences},
    volume = {11},
    number = {10},
    pages = {933-941},
    year = {2016},
    issn = {1815-932x},
    doi = {rjasci.2016.933.941},
    url = {https://makhillpublications.co/view-article.php?issn=1815-932x&doi=rjasci.2016.933.941},
    author = {Eman and},
    keywords = {Neural network approximation,saturation problem,spaces,direct inequality},
    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.}
    }