We introduce a new class of harmonici multivalent functions define by generalized Rucheweyh derivative operator. We also obtain several interesting propertiesi such as sharp coefficienit estimates, distortioni bound, extreme points, Hadamardi product and other several results. Derivative; extreme points.
A new class of higher derivatives for harmonic univalent functions defined by a generalized fractional integral operator inside an open unit disk E is the aim of this paper.
The aim of this paper is to approximate multidimensional functions by using the type of Feedforward neural networks (FFNNs) which is called Greedy radial basis function neural networks (GRBFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy radial basis function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result is published in [16]).
In this paper we study and design two feed forward neural networks. The first approach uses radial basis function network and second approach uses wavelet basis function network to approximate the mapping from the input to the output space. The trained networks are then used in an conjugate gradient algorithm to estimate the output. These neural networks are then applied to solve differential equation. Results of applying these algorithms to several examples are presented
The main goal of this paper is to introduce the higher derivatives multivalent harmonic function class, which is defined by the general linear operator. As a result, geometric properties such as coefficient estimation, convex combination, extreme point, distortion theorem and convolution property are obtained. Finally, we show that this class is invariant under the Bernandi-Libera-Livingston integral for harmonic functions.
Results of charge, neutron and matter densities and related form factors for one- proton halo nucleus 8B are presented using a two- frequency shell model approach. We choose a model space for the core of 7Be different from that of the extra one valence proton. One configuration is assumed for the outer proton to be in 1p1/2 - shell. The results of the matter density distributions are compared with those fitted to the experimental data. The calculated proton and matter density distributions of this exotic nucleus exhibit a long tail behavior, which is considered as a distinctive feature of halo nuclei. Elastic electron scattering form factors of this exotic nucleus are also studied. The effects of
... Show MoreThe bound radial wave functions of Cosh potential which are the solutions to the radial part of Schrodinger equation are solved numerically and used to compute the size radii; i.e., the root-mean square proton, neutron, charge and matter radii, ground density distributions and elastic electron scattering charge form factors for nitrogen isotopes 14,16,18,20,22N. The parameters of such potential for the isotopes under study have been opted so as to regenerate the experimental last single nucleon binding energies on Fermi's level and available experimental size radii as well.
The ground charge density distributions (CDD), elastic charge form factors and proton, charge, neutron, and matter root mean square (rms) radii for stable 40Ca and 48Ca have been calculated using single-particle radial wave functions of Woods-Saxon (WS) and harmonic-oscillator (HO) potentials. Different central potential depths are used for each subshell which is adjusted so as to reproduce the experimental single-nucleon binding energies. An excellent agreement between the calculated rms charge radii and experimental data are found for both nuclei using WS and HO potentials. The calculated proton rms radii for 40Ca are found to be in good agreement with experiment data using both WS and HO potentials while the results for 48Ca showed an ov
... Show MoreAbstract:In this paper, some probability characteristics functions (moments, variances,convariance, and spectral density functions) are found depending upon the smallestvariance of the solution of some stochastic Fredholm integral equation contains as aknown function, the sine wave function