In this paper a new approach for evaluation of composite system reliability of power systems is described. In this method Monte Carlo Simulation (MCS) is combined with Multilabel Radial Basis Function (MLrbf) classifi...
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ISBN:
(纸本)9781538635964
In this paper a new approach for evaluation of composite system reliability of power systems is described. In this method Monte Carlo Simulation (MCS) is combined with Multilabel Radial Basis Function (MLrbf) classifier to compute system reliability indices. MLrbf is a classification technique in which target vector of each instance is assigned into multiple classes. In this study MLrbf is used to characterize states of a complete power system (failure or success) without requiring optimal power flow (OPF) analysis. As a result, the computational burden of the reliability evaluation analysis to perform OPF is eliminated. For illustration, the proposed method is applied to the IEEE Modified Reliability Test System (IEEE-MRTS). The obtained results demonstrate that MLrbfalgorithm in reliability evaluation provides good performance in classification accuracy while reducing computation time dramatically.
In this paper, we proposed an hybrid optimal radial-basis function (rbf) neural network for approximation and illumination invariant image segmentation. Unlike other rbf learning algorithms, the proposed paradigm intr...
详细信息
ISBN:
(纸本)9781424496365
In this paper, we proposed an hybrid optimal radial-basis function (rbf) neural network for approximation and illumination invariant image segmentation. Unlike other rbf learning algorithms, the proposed paradigm introduces a new way to train rbf models by using optimal learning factors (OLFs) to train the network parameters, i.e. spread parameter, kernel vector and a weighted distance measure (DM) factor to calculate the activation function. An efficient second order Newton's algorithm is proposed for obtaining multiple OLF's (MOLF) for the network parameters. The weights connected to the output layer are trained by a supervised-learningalgorithm based on orthogonal least square (OLS). The error obtained is then back-propagated to tune the rbf parameters. By applying rbf network for approximation on some real-life datasets and classification to reduce illumination effects of image segmentation, the results show that the proposed rbf neural network has fast convergence rates combining with low computational time cost, allowing it a good choice for real-life application such as image segmentation.
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