Hybrid model is a popular forecasting model in renewable energy related forecasting applications. Wind speed forecasting, as a common application, requires fast and accurate forecasting models. This paper introduces an Empirical Mode Decomposition (EMD) followed by a k Nearest Neighbor (kNN) hybrid model for wind speed forecasting. Two configurations of EMD-kNN are discussed in details: an EMD-...

In recent years, K-Nearest Neighbor (KNN) has demonstrated excellent performance in a variety of pattern recognition problems. In this paper, we proposed modified KNN for the Chinese minority scripts classification, using wavelet energy distribution and wavelet energy proportions features generated from the discrete wavelet multiresolution decomposition. The experimental results show that the m...

in this paper coupled nonlinear equations of motion of a suspended cable with time dependent tension and velocity are derived by using hamilton’s principal. a modal analysis for a stationary sagged cable is initially carried out in order to identify the dynamic system. the natural solution is directed to compute the natural frequencies and mode shapes of the free vibration of a suspended cable....

background : kernel smoothing method is a non-parametric or graphical method for statistical estimation. in the present study was used a kernel smoothing method for finding the death hazard rates of patients with acute myocardial infarction. methods : by employing non-parametric regression methods, the curve estimation, may have some complexity.  in this article, four indices of epanechnikov, b...

Identifying the queried object, from a large volume of given uncertain dataset, is a tedious task which involves time complexity and computational complexity. To solve these complexities, various research techniques were proposed. Among these, the simple, highly efficient and effective technique is, finding the K-Nearest Neighbor (kNN) algorithm. It is a technique which has applications in vari...