Combining different physical and / or statistical predictive algorithms for Nuclear Power Plant (NPP) components into an ensemble can improve the robustness and accuracy of the prediction. In this paper, an ensemble approach is proposed for prediction of time series data based on a modified Probabilistic Support Vector Regression (PSVR) algorithm. We propose a modified Radial Basis Function (RB...

Least squares support vector machines (LS-SVMs), a nonlinear kemel based machine was introduced to investigate the prospects of application of this approach in modelling water vapor and carbon dioxide fluxes above a summer maize field using the dataset obtained in the North China Plain with eddy covariance technique. The performances of the LS-SVMs were compared to the corresponding models obta...

This paper shows a few novel calculations for wind speed estimation, which is focused around soft computing. The inputs of to the estimators are picked as the wind turbine power coefficient, rotational rate and blade pitch angle. Polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) technique to estimate the wind speed in this study. In...

The effect of early fault vibration signals from rotating machinery is weak and easily drowned out by intense noise. Therefore, it still a great challenge to make diagnosis. An intelligent diagnosis method for proposed based on the parameter optimization variational mode decomposition (VMD) deep multi-kernel extreme learning machine (DMKELM). Firstly, improved whale algorithm (IWOA) designed in...

Stein variational gradient descent (SVGD) and its variants have shown promising successes in approximate inference for complex distributions. In practice, we notice that the kernel used SVGD-based methods has a decisive effect on empirical performance. Radial basis function (RBF) with median heuristics is common choice previous approaches, but unfortunately this proven to be sub-optimal. Inspir...

Recent works in speech recognition technology, classification techniques is focused on models, such as support vector machines (SVMs), in order to improve the generalization ability of the machine learning for noisy environments. However kernel function plays a vital role in the generalization ability of the SVMs. This paper address, the issue of noise robustness for an Automatic Speech Recogni...

In this paper, a meshless method for solving the eigenproblems of plate vibration using the radial basis function (RBF) is proposed. By employing the RBF in the imaginary-part fundamental solution, spurious eigenequations in conjunction with the true ones are obtained at the same time. Mathematical analysis for the appearance of spurious eigenequations by using degenerate kernel and circulant i...

In this paper, a meshless method for the acoustic eigenfrequencies using radial basis function (RBF) is proposed. The coefficients of influence matrices are easily determined by the two-point functions. In determining the diagonal elements of the influence matrices, two techniques, limiting approach and invariant method, are employed. Based on the RBF in the imaginary-part kernel, the method re...

Problems faced in contemporary practice should be understood to improve requirements engineering processes. System requirements are descriptions of services provided by a system and operational constraints. NonFunctional Requirements (NFR) defines overall qualities/attributes of the system. NFR analysis is a significant activity in this branch of engineering. In this study, a methodology for cl...

In this paper, we consider convex quadratic semidefinite optimization problems and provide a primal-dual Interior Point Method (IPM) based on a new kernel function with a trigonometric barrier term. Iteration complexity of the algorithm is analyzed using some easy to check and mild conditions. Although our proposed kernel function is neither a Self-Regular (SR) fun...