A fault diagnosis method for sensor fault based on ensemble empirical mode decomposition (EEMD) energy entropy and optimized structural parameters least squares support vector machine (LSSVM) is put forward in this paper. Firstly, the original output fault signals are pretreatment with EEMD, and then the EEMD energy entropy is extracted as the fault feature vector. Then the radial basis functio...

To reduce the influence of random fluctuation on wind power prediction, a new ultra-short-term prediction model, based wavelet decomposition (WD), variational mode (VMD), and least-squares support vector machine (LSSVM), is proposed in this paper. The method double LSSVM, where sequence decomposed by WD into low- high-frequency components, which are further VMD to obtain many modal components w...

In the era of very rapidly advancing technology like today, both internet and computerization have made various corporate agencies or investors start thinking about importance stock market in their capital division. Previously there were purchases by company's capital, such: gold, land, buildings, production machines, but at this time purchase shares should also to attract attention these are l...

In order to improve the accuracy of positioning wireless sensor and aiming at optimizing the least square support vector machine’s parameters, a sensor node positioning method based on fisher fishing optimization – the least square support vector machine is proposed. First of all, learning samples of the three-dimensional wireless sensor positioning model are established, and then the least squ...

In this paper, an improved version of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) is proposed for feature extraction to classify the ischemic beats from electrocardiogram (ECG) signal. The Fuzzy C-Means (FCM) and Genetic Algorithm (GA) is combined with PCA and ICA to extract more relevant features; the proposed methods are named as Fuzzy-Genetic based PCA (FGPCA)...

Successful river flow forecasting is a major goal and an essential procedure that is necessary in water resource planning and management. There are many forecasting techniques used for river flow forecasting. This study proposed a hybrid model based on a combination of two methods: Self Organizing Map (SOM) and Least Squares Support Vector Machine (LSSVM) model, referred to as the SOM-LSSVM mod...

PCA and ICA are two powerful techniques for feature extraction. In addition, fuzzy c-means clustering (FCM) is among considerable techniques for data reduction. In other words, the aim of using FCM is to decrease the number of segments by grouping similar segments in training data. In this work, an improved version of PCA and ICA is proposed for feature extraction to classify the ischemic beats...

Model selection is critical to least squares support vector machine (LSSVM). A major problem of existing model selection approaches of LSSVM is that the inverse of the kernel matrix need to be calculated with O(n) complexity for each iteration, where n is the number of training examples. It is prohibitive for the large scale application. In this paper, we propose an approximate approach to mode...