Numerous studies show that it is reasonable and effective to apply decomposition technology deal with the complex carbon price series. However, existing research ignores residual term containing information after applying single technique. Considering demand for higher accuracy of series prediction following path, this paper proposes a new hybrid model VMD-CEEMDAN-LSSVM-LSTM, which combines qua...

Data recorded from monitoring the health condition of industrial equipment are often high-dimensional, nonlinear, nonstationary and characterised by high levels uncertainty. These factors limit efficiency machine learning techniques to produce desirable results when developing effective fault classification frameworks. This paper sought propose a hybrid artificial intelligent predictive mainten...

This paper explores the Support Vector Machine and Least Square Support Vector Machine models in stock forecasting. Three prevailing forecasting techniques General Autoregressive Conditional Heteroskedasticity (GARCH), Support Vector Regression (SVR) and Least Square Support Vector Machine (LSSVM) are combined with the wavelet kernel to form three novel algorithms Wavelet-based GARCH (WL_GARCH)...

this paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (lssvm). two wavelet selection criteria maximum energy to shannon entropy ratio and maximum relative wavelet energy are used and compared to select an appropriate wavelet for feature extraction. the fault diagnosis method co...

this paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (lssvm). two wavelet selection criteria maximum energy to shannon entropy ratio and maximum relative wavelet energy are used and compared to select an appropriate wavelet for feature extraction. the fault diagnosis method co...

Tian Zhongda, Li Shujiang, Wang Yanhong, Zhang Chao College of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China Corresponding author: Tian Zhong-da,Email:[email protected] Abstract: In order to improve control performance of nonlinear systems, a predictive control method based on improved free search algorithm and least square support vector machi...

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...