• Honam Mathematical Journal
• /
• v.31 no.2
• /
• pp.147-156
• /
• 2009

In this paper, we actually construct the simultaneous approximation by neural networks to a differentiable function. To do this, we first construct a polynomial approximation using the Fejer sum and then a simultaneous neural network approximation with the squashing activation function. We also give numerical results to support our theory.

• East Asian mathematical journal
• /
• v.24 no.2
• /
• pp.169-176
• /
• 2008

The aim of this paper is to prove a common fixed point theorem in normed linear spaces for noncompatible, discontinuous mappings without assuming completeness of the space. We also give an application of our theorem to best approximation theory.

• The Pure and Applied Mathematics
• /
• v.8 no.1
• /
• pp.9-14
• /
• 2001

In some problems of abstract approximation theory the approximating set depends on some parameter p. In this paper, we make a set M(f) depends on the element f, $\phi$ and then best approximations are sought from a subset M(f) of M.

• Interaction and multiscale mechanics
• /
• v.1 no.4
• /
• pp.423-435
• /
• 2008

This paper employs the reproducing kernel (RK) approximation for evaluation of field theory-based incompatibility tensor in a polycrystalline plasticity simulation. The modulation patterns, which is interpreted as mimicking geometrical-type dislocation substructures, are obtained based on the proposed method. Comparisons are made using FEM and RK based approximation methods among different support sizes and other evaluation conditions of the strain gradients. It is demonstrated that the evolution of the modulation patterns needs to be accurately calculated at each time step to yield a correct physical interpretation. The effect of the higher order strain derivative processing zone on the predicted modulation patterns is also discussed.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.33 no.8
• /
• pp.753-759
• /
• 2009

Feed-forward neural networks have been widely used as function approximation tools in the context of global approximate optimization. In the present study, a wavelet neural network (WNN) which is based on wavelet transform theory is suggested as an alternative to a traditional back-propagation neural network (BPN). The basic theory of wavelet neural network is briefly described, and approximation performance is tested using a nonlinear multimodal function and a composite rotor blade analysis problem. Laplacian of Gaussian function, Mexican function, and Morlet function are considered during the construction of WNN architectures. In addition, approximation results from WNN are compared with those from BPN.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.23 no.6 s.165
• /
• pp.968-978
• /
• 1999

The effective elastic properties of materials containing spherical inclusions were calculated by the elastic wave scattering theory. In the formulation additional scattering fields by the presence of random multiple scatterers that affects the effective properties were found by the single scattering approximation. In calculating the scattering fields the ensemble average on the displacements and strains inside the scatterer was found from the static approximation at long wavelength limit. The displacements were assumed to be equal to the incident field, while the strains were calculated by Eshelby's equivalent inclusion principle on the single inclusion problem. Four different models were considered and they reflected different degrees of multiple scattering effects based on the approximation introduced in the process of embedding the inclusion in the matrix. The expressions for the effective elastic constants were given in each model, and their relations to the results obtained from other scattering theory and elasticity theory were discussed. The theoretical predictions were compared with experimental results on the epoxy matrix composites containing tungsten particles of different sizes and volume fractions

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.12
• /
• pp.4567-4583
• /
• 2021

This study proposes an analytical approximation algorithm based on extreme value theory (EVT) for the inverse of the power of the incomplete Gamma function. First, the Gumbel function is used to approximate the power of the incomplete Gamma function, and the corresponding inverse problem is transformed into the inversion of an exponential function. Then, using the tail equivalence theorem, the normalized coefficient of the general Weibull distribution function is employed to replace the normalized coefficient of the random variable following a Gamma distribution, and the approximate closed form solution is obtained. The effects of equation parameters on the algorithm performance are evaluated through simulation analysis under various conditions, and the performance of this algorithm is compared to those of the Newton iterative algorithm and other existing approximate analytical algorithms. The proposed algorithm exhibits good approximation performance under appropriate parameter settings. Finally, the performance of this method is evaluated by calculating the thresholds of space-time block coding and space-frequency block coding pattern recognition in multiple-input and multiple-output orthogonal frequency division multiplexing. The analytical approximation method can be applied to other related situations involving the maximum statistics of independent and identically distributed random variables following Gamma distributions.

• Bulletin of the Korean Mathematical Society
• /
• v.20 no.2
• /
• pp.75-79
• /
• 1983

In this paper we shall continue the study of the approximation theorems in the double pseudodifferential operators as in the single pseudodifferential operators.

• Applied Microscopy
• /
• v.33 no.3
• /
• pp.169-178
• /
• 2003

In this review, theoretical approaches of imaging and diffraction in electron microscopy are introduced which allows the diffraction patterns and images to be treated with equal facility and emphasized the relationships between them. The coherent wave optics, incoherent wave imaging theory were introduced. The idea of Abbe theory was also introduced. Varoius phase contrast theories in small angle approximation were derived including the wave theory on Multi-component system.

• Journal of applied mathematics & informatics
• /
• v.17 no.1_2_3
• /
• pp.701-707
• /
• 2005

Delta sequences play an important role in convergence and approximation theory. Much of classical approximation theory is based on delta sequence. The rate of convergence of certain types of these sequences in Sobolev spaces has recently been studied. Here we estimate convergence rate of convolution type delta sequence in higher dimension.