Application of support vector neural network with variational mode decomposition for exchange rate forecasting

2018 ◽  
Vol 23 (16) ◽  
pp. 6995-7004 ◽  
Author(s):  
Yungao Wu ◽  
Jianwei Gao
Keyword(s):  
Neural Network ◽  
Exchange Rate ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Exchange Rate Forecasting ◽  
Mode Decomposition

scholarly journals Recognition and Classification of Incipient Cable Failures Based on Variational Mode Decomposition and a Convolutional Neural Network

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 2005 ◽  
Author(s):  
Jiaying Deng ◽  
Wenhai Zhang ◽  
Xiaomei Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Bp Neural Network ◽  
Nearest Neighbor ◽  
Feature Vector ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
K Nearest Neighbor ◽  
Mode Decomposition

To avoid power supply hazards caused by cable failures, this paper presents an approach of incipient cable failure recognition and classification based on variational mode decomposition (VMD) and a convolutional neural network (CNN). By using VMD, the original current signal is decomposed into seven modes with different center frequencies. Then, 42 features are extracted for the seven modes and used to construct a feature vector as input of the CNN to classify incipient cable failure through deep learning. Compared with using the original signals directly as the CNN input, the proposed approach is more efficient and robust. Experiments on different classifiers, namely, the decision tree (DT), K-nearest neighbor (KNN), BP neural network (BP) and support vector machine (SVM), and show that the CNN outperforms the other classifiers in terms of accuracy.

scholarly journals Hydrological peaks evaluation at chitral and tarbela stations, Pakistan, using combined Bayesian regularized neural network and signal difference average based variational mode decomposition method: a case study

2021 ◽  
Author(s):  
Mehwish Shafi Khan ◽  
Syed Ahmad Hassan
Keyword(s):  
Neural Network ◽  
River Flow ◽  
Irrigation System ◽  
Moving Average ◽  
Indus Basin ◽  
Gradient Boosting ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Decomposition Approach ◽  
Mode Decomposition

Abstract Pakistan being an agricultural country highly depends on its natural water resources originate from the upper regions of Hindu Kush-Karakoram-Himalaya Mountains and nourish one of the world's largest Indus Basin irrigation system. This paper presents streamflow modelling and forecasting using signal difference average (SDA) based variational mode decomposition (VMD) combined with machine learning (ML) methods at Chitral and Tarbela stations on the Indus River network. For this purpose, VMD based; random forest (VMD-RF), gradient boosting machine (VMD-GBM) and Bayesian regularized neural network (VMD-BRNN) have been opted. Moreover, traditional time series flow model that is seasonal autoregressive integrated moving average (SARIMA) and classical decomposition approach with particle swarm optimization-based support vector regression (PSO-SVR) are considered as benchmark models for comparison. The results show that overall, VMD-BRNN performed best, followed by VMD-GBM and VMD-RF, whereas, SARIMA and PSO-SVR ranked last. Overall, SARIMA and PSO-SVR are failing to capture most of the peaks even during training period. Whereas, hybridization of VMD and ML methods has shown increased robustness of the models. The results show that the influential role of the high dimensional components and robustness on the river flow may be explored by most optimum SDA based VMD signals hybrid with BRNN method.

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