Ditzain-Totik modulus of smoothness for the fractional derivative of functions in $L_p$ space of the partial neural network | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 13، شماره 1، خرداد 2022، صفحه 3305-3317 اصل مقاله (419.61 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.6083 | ||
| نویسندگان | ||
| Amenah Hassan Ibrahim* 1؛ Eman Samir Bhaya2؛ Eman Ali Hessen1 | ||
| 1Department of Mathematics, Collage of Sciences, AL-Mustansiriyah University, Baghdad, Iraq | ||
| 2Department of Mathematics, Collage of Education for Pure Sciences, University of Babylon, Iraq | ||
| چکیده | ||
| Some scientists studied the weighted approximation of the partial neural network, but in this paper, we studied the weighted Ditzain-Totik modulus of smoothness for the fractional derivative of functions in $L_p$ of the partial neural network and this approximation of the real-valued functions over a compressed period by the tangent sigmoid and quasi-interpolation operators. These approximations measurable left and right partial Caputo models of the committed function. Approximations are bitmap with respect to the standard base. Feed-forward neural networks with a single hidden layer. Our higher-order fractal approximation results in better convergence than normal approximation with some applications. All proved results are in $L_p[X]$ spaces, where $0{<}p{<}1$ | ||
| کلیدواژهها | ||
| approximation؛ Ditzain-Totik modulus؛ higher-order fractal approximation؛ partial Caputo models؛ partial neural network؛ Sobolev space | ||
| مراجع | ||
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