In this article the approach was used to coherent assessment based on the intensity of air pollution sources impact on the impurity concentration at a few fixed points to monitor air quality. The numerical analogue ...
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In this article the approach was used to coherent assessment based on the intensity of air pollution sources impact on the impurity concentration at a few fixed points to monitor air quality. The numerical analogue of Duhamers theorem was used to describe processes of propagation of impurity in the atmosphere. Such approach allows you to count on essential increase of calculation accuracy based on mathematical models of reasonable complexity. The inverse problem of pollutants propagation in the atmosphere based on the measurements of the impurity concentration in stationary or mobile control points was solved by the sequential function approximation The solution was presented in the form of a digital filter.
A recently published generalized growing and pruning (GGAP) training algorithm for radial basis function (RBF) neural networks is studied and modified. GGAP is a resource-allocating network (RAN) algorithm, which mean...
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A recently published generalized growing and pruning (GGAP) training algorithm for radial basis function (RBF) neural networks is studied and modified. GGAP is a resource-allocating network (RAN) algorithm, which means that a created network unit that consistently makes little contribution to the network's performance can be removed during the training. GGAP states a formula for computing the significance of the network units, which requires a d-fold numerical integration for arbitrary probability density function p(x) of the input data x(x is an element of R-d). In this work, the GGAP formula is approximated using a Gaussian mixture model (GMM) for p(x) and an analytical solution of the approximated unit significance is derived. This makes it possible to employ the modified GGAP for input data having complex and high-dimensional p(x), which was not possible in the original GGAP. The results of an extensive experimental study show that the modified algorithm outperforms the original GGAP achieving both a lower prediction error and reduced complexity of the trained network.
A computational method for the solution of differential equations is proposed. With this method an accurate approximation is built by incremental additions of optimal local basis functions. The parallel direct search ...
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A computational method for the solution of differential equations is proposed. With this method an accurate approximation is built by incremental additions of optimal local basis functions. The parallel direct search ...
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A computational method for the solution of differential equations is proposed. With this method an accurate approximation is built by incremental additions of optimal local basis functions. The parallel direct search software package (PDS), that supports parallel objective function evaluations, is used to solve the associated optimization problem efficiently. The advantage of the method is that, although it resembles adaptive methods in computational mechanics, an a priori grid is not necessary. Moreover, the traditional matrix construction and evaluations are avoided. Computational cost is reduced while efficiency is enhanced by the low-dimensional parallel-executed optimization and parallel function evaluations. In addition, the method should be applicable to a broad class of interpolation functions. Results and global convergence rates obtained for one-and two-dimensional boundary value problems are satisfactorily compared to those obtained by the conventional Galerkin finite element method. (C) 1997 John Wiley & Sons, Ltd.
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