In this research, we present a nonparametric approach for the estimation of a copula density using different kernel density methods. Different functions were used: Gaussian, Gumbel, Clayton, and Frank copula, and through various simulation experiments we generated the standard bivariate normal distribution at samples sizes (50, 100, 250 and 500), in both high and low dependency. Different kernel methods were used to estimate the probability density function of the copula with marginal of this bivariate distribution: Mirror – Reflection (MR), Beta Kernel (BK) and transformation kernel (KD) method, then a comparison was carried out between the three methods with all the experiments using the integrated mean squared error. Furthermore, some charts were used to support this comparison such as copula contours to spread the correlation before and after estimations. The simulation results show preference of the transformation kernel estimators (KDE) among all the estimation methods, which also proved that copulas are highly flexible models for high dependency especially of the Gaussian type.
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The aim of this paper is to approximate multidimensional functions f∈C(R^s) by developing a new type of Feedforward neural networks (FFNS) which we called it Greedy ridge function neural networks (GRGFNNS). Also, we introduce a modification to the greedy algorithm which is used to train the greedy ridge function neural networks. An error bound are introduced in Sobolov space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result in [1]).
This study was undertaken to provide more insight on the optimum injection temperature used for the production of PE crates, thereby saving time and money, and improving part quality. The work included processing trails of HDPE crates in an injection
molding machine at five temperatures ranged from 220 to 300°C. Both Rheological and mechanical characterization was conducted in order to understand the effect of injection temperature on the properties of crates. Oven aging was also applied for (4 weeks) to evaluate the long-term thermal stability. The results revealed that producing the crates at a temperature range of (260-280 °C) gives the best rheological and mechanical result. The lowest drop in thermal stability has been observed
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The numerical response of Chrysoperla mutata MacLachlan was achieved by exposing the larvae of the predators to various densities of dubas nymphs Ommatissus lybicus DeBerg. Survival rate of predators’ larvae and adults emergence increased with increasing consumption . Repriductive response of predator was highly correlated with the amount of food consumed (+0.996).
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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]).
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The possible effect of the collective motion in heavy nuclei has been investigated in the framework of Nilson model. This effect has been searched realistically by calculating the level density, which plays a significant role in the description of the reaction cross sections in the statistical nuclear theory. The nuclear level density parameter for some deformed radioisotopes of (even- even) target nuclei (Dy, W and Os) is calculated, by taking into consideration the collective motion for excitation modes for the observed nuclear spectra near the neutron binding energy. The method employed in the present work assumes equidistant spacing of the collective coupled state bands of the considered isotopes. The present calculated results for f
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