This paper is devoted to the analysis of time series using the Empirical Mode Decomposition (EMD) method. This method decomposes the analyzed time series into a small set of narrow-band components (modes) that fully represent the original time series. The modified EMD method that eliminates excessive changes of individual mode periods is proposed and evaluated on one example application of indu...

The empirical mode decomposition (EMD) is well known for predicting wind speed.However, but the joint application of relevance vector machine (RVM) and empirical mode decomposition in wind speed forecasting is seldom found in the field. This paper proposes a relevance vector machine model based on empirical mode decomposition to predict the wind speed. Before the wind speed forecasting with RVM...

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 7 May 2021Accepted: 25 August 2021Published online: 04 January 2022Keywordsdeep learning, mathematical morphology, hyperspectral image processing, remote sensingAMS Subject Headings68T07, 94A12, 68U10Publication DataISSN (online): 1936-4954Publisher: Society for Industria...

In this work we explore the multivariate empirical mode decomposition combined with a Neural Network classifier as technique for face recognition tasks. Images are simultaneously decomposed by means of EMD and then the distance between the modes of the image and the modes of the representative image of each class is calculated using three different distance measures. Then, a neural network is t...