Mixed Kernel Based Extreme Learning Machine For Electric

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Mixed kernel based extreme learning machine for electric ...

27/10/2018  EMD-Mixed-ELM first uses the empirical mode decomposition to decompose the load series for capturing the complicated features of the electric load and de-noising the data. Considering that the performance of extreme learning machine (ELM) is greatly influenced by the choice of kernel, the mixed kernel method is proposed for ELM.

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Mixed kernel based extreme learning machine for electric ...

EMD-Mixed-ELM first uses the empirical mode decomposition to decompose the load series for capturing the complicated features of the electric load and de-noising the data. Considering that the performance of extreme learning machine (ELM) is greatly influenced by the choice of kernel, the mixed kernel method is proposed for ELM. The mixed ...

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Mixed kernel based extreme learning machine for electric ...

Chen et al. [40] combined the mixed kernel method and extreme learning machine to forecast short-term electric load, and experimental results indicated that the proposed model was superior to ...

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Mixed kernel based extreme learning machine for electric ...

EMD-Mixed-ELM first uses the empirical mode decomposition to decompose the load series for capturing the complicated features of the electric load and de-noising the data. Considering that the performance of extreme learning machine (ELM) is greatly influenced by the choice of kernel, the mixed kernel method is proposed for ELM. The mixed kernel combines the RBF kernel and the UKF kernel. The ...

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Mixed kernel based extreme learning machine for electric ...

ACCEPTED MANUSCRIPT ACCEPTED MANUSCRIPT Mixed kernel based extreme learning machine for electric load forecasting Yanhua Chen a,b,, Marius Kloft b, Yi Yang c, Caihong Li c, Lian Li c a School of Information Engineering, Zhengzhou University, Zhengzhou 450000, P.R. China b Department of Computer Science Humboldt University of Berlin 10099 Berlin, Germany c School of

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Mixed-kernel based weighted extreme learning machine for ...

19/05/2016  The proposed mixed-kernel based weighted extreme learning machine (ELM) is based on the unified ELM. Then a brief review of the unified ELM will be provided in this section. 2.1. Unified ELM. The network architecture of ELM, intuitively speaking, can be regarded as generalized SLFNs. However, in the basic ELM, hidden nodes can be generated ...

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An Extreme Learning Machine Based on the Mixed Kernel ...

In this work, a novel extreme learning machine based on the mixed kernel function of triangular kernel and generalized Hermite Dirichlet kernel (Tri-H KELM) has been put forward, which introduces the triangular Hermite kernel function to kernel extreme learning machine algorithm. Because the presented kernel has only one parameter chosen from a small set of integers, the parameter

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Kernel fusion based extreme learning machine for cross ...

01/09/2017  In this paper, TransM-RKELM (Transfer learning mixed and reduced kernel Extreme Learning Machine) is proposed to tackle the cross-location human activity recognition problem based on a single accelerometer embedded wearable device. Comparing with ELM and RKELM method, results show that better classification has been obtained by the proposed method. M-RKELM, as a fast online sequential learning ...

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Extreme learning machine with kernel model based on deep ...

12/01/2016  Extreme learning machine (ELM) proposed by Huang et al. is a learning algorithm for single-hidden layer feedforward neural networks (SLFNs). ELM has the advantage of fast learning speed and high efficiency, so it brought into public focus. Later someone developed regularized extreme learning machine (RELM) and extreme learning machine with kernel (KELM). But they are the

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A Kernel-Based Extreme Learning Machine Framework for ...

11/07/2019  In this paper, kernel-based extreme learning machine (KELM) has been used as a classifier and multiview with adaptive maximum disagreement (MV-AMD) technique has been used for query function. For comparison, KELM has also been integrated with random sampling-based active learning (RS-AL). In the following sections, RS-AL, MV-AMD and KELM have been described. Random Sampling-Based

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Mixed-kernel based weighted extreme learning machine for ...

Balanced dataset has been utilized by the previous human activity recognition algorithms to train the classifier. However, imbalanced dataset are ubiquitous in human activity recognition, especiall...

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Mixed-kernel based weighted extreme learning machine for ...

In order to reduce the influence of the imbalance datasets problem, the mixed-kernel based weighted extreme learning machine (MK-WELM) has been proposed in this paper. Considering that the performance of extreme learning machine (ELM) is greatly influenced by the choice of kernel, the mixed kernel method is proposed for ELM. In order to deal with the imbalanced problem, the cost

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An Extreme Learning Machine Based on the Mixed Kernel ...

An Extreme Learning Machine Based on the Mixed Kernel Function of Triangular Kernel and Generalized Hermite Dirichlet Kernel. Senyue Zhang and Wenan Tan. Discrete Dynamics in Nature and Society, 2016, vol. 2016, 1-11 Abstract: According to the characteristics that the kernel function of extreme learning machine (ELM) and its performance have a strong correlation, a novel extreme learning ...

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Parameters Selection of Kernel Based Extreme Learning ...

Jibei Electric Power Maintenance Company,102400Beijing, China *Corresponding author(E-mail: [email protected]) Abstract The generalization performance of kernel based extreme learning machine (KELM) with Gaussian kernel are sensitive to the parameters combination (C, γ). The best generalization performance of KELM with Gaussian kernel is usually achieved in a very narrow range of such ...

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Deformed Kernel Based Extreme Learning Machine

Deformed Kernel Based Extreme Learning Machine Zhang Chen School of Computer Science and Technology,China University of Mining and Technology, XuZhou,221116,China Email: [email protected] Xia Shi Xiong and Liu Bing School of Computer Science and Technology,China University of Mining and Technology, XuZhou,221116,China Email: [email protected], [email protected] Abstract—The extreme learning ...

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Kernel-Extreme Learning Machine-Based Fault Location in ...

Results of the kernel extreme learning machine are compared with other popular single-hidden layer feedforward network-based techniques, such as extreme learning machines, support vector machines, and relevance vector machines. Performances of these single-hidden layer feedforward networks are verified on two power systems: (1) Two-area equivalent system, and (2) 12-bus system modeled in ...

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Kernel-based Machine Learning with Multiple Sources of Information. Information Technology: Distinguished Dissertations, 55(2):76-80, 2013. 2012. M. Kloft and P. Laskov. Security Analysis of Online Centroid Anomaly Detection. Journal of Machine Learning Research (JMLR), 13(Dec):3647-3690, 2012. M. Kloft and G. Blanchard.

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Gearbox fault diagnosis through quantum particle swarm ...

Common intelligent classification algorithms include neural network [1, 2], support vector machine (SVM) [3-5], kernel extreme learning machine (KELM) [6, 7], deep learning [8-10] and other methods. Wang [2] proposed a fault diagnosis method based on RDGWPR-MSE and PNN, which is used to realize the automatic fault identification of electric submersible pump. Wang and Yan [4] used the energy of ...

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Feng Wang's homepage - GitHub Pages

Exploring Mutual Information based Sentimental Analysis with Kernel based Extreme Learning Machine for Stock Prediction. Soft Computing, 2017, 21 (12):3193-3205. (SCI, JCR Q2, IF: 2.472). Xiaodong Li, Xiaotie Deng, Shanfeng Zhu, Feng Wang*, Haoran Xie. An Intelligent Market Making Stragegy in Algorithmic Trading.

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Gaussian Process Regression With Automatic Relevance ...

02/10/2019  Battery calendar aging prediction is of extreme importance for developing durable electric vehicles. This article derives machine learning-enabled calendar aging prediction for lithium-ion batteries. Specifically, the Gaussian process regression (GPR) technique is employed to capture the underlying mapping among capacity, storage temperature, and state-of-charge. By modifying the

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An Extreme Learning Machine Based on the Mixed Kernel ...

An Extreme Learning Machine Based on the Mixed Kernel Function of Triangular Kernel and Generalized Hermite Dirichlet Kernel. Senyue Zhang and Wenan Tan. Discrete Dynamics in Nature and Society, 2016, vol. 2016, 1-11 Abstract: According to the characteristics that the kernel function of extreme learning machine (ELM) and its performance have a strong correlation, a novel extreme learning ...

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Kernel Based Machine Learning For Molecular Energy Estimation

Mixed kernel based extreme learning machine for electric ... Multiple kernel learning refers to a set of machine learning methods that use a predefined set of kernels and learn an optimal linear or non-linear combination of kernels as part of the algorithm. Reasons to use multiple kernel learning include a) the ability to select for an optimal kernel and parameters from a larger set of kernels ...

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Deformed Kernel Based Extreme Learning Machine

Deformed Kernel Based Extreme Learning Machine Zhang Chen School of Computer Science and Technology,China University of Mining and Technology, XuZhou,221116,China Email: [email protected] Xia Shi Xiong and Liu Bing School of Computer Science and Technology,China University of Mining and Technology, XuZhou,221116,China Email: [email protected], [email protected] Abstract—The extreme learning ...

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Kernel-Extreme Learning Machine-Based Fault Location in ...

Results of the kernel extreme learning machine are compared with other popular single-hidden layer feedforward network-based techniques, such as extreme learning machines, support vector machines, and relevance vector machines. Performances of these single-hidden layer feedforward networks are verified on two power systems: (1) Two-area equivalent system, and (2) 12-bus system modeled in ...

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Multi label Learning Based on Kernel Extreme Learning Machine

Besides, the Cholesky matrix decomposition inverse method is adopted to calculate the network output weight of the kernel extreme learning machine.. In particular, in terms

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Recognition Model Based Feature Extraction and Kernel ...

Recognition Model Based Feature Extraction and Kernel Extreme Learning Machine for High Dimensional Data ... The Model Reference Adaptive Control of the DC Electric Drive System p.2030. A New Design of Variable Traction Tracked System p.2036. Monotone Constraint Model Applied to the Reliability Growth Evaluation of Electromechanical Products p.2042. Home Advanced Materials

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Feng Wang's homepage - GitHub Pages

Exploring Mutual Information based Sentimental Analysis with Kernel based Extreme Learning Machine for Stock Prediction. Soft Computing, 2017, 21 (12):3193-3205. (SCI, JCR Q2, IF: 2.472). Xiaodong Li, Xiaotie Deng, Shanfeng Zhu, Feng Wang*, Haoran Xie. An Intelligent Market Making Stragegy in Algorithmic Trading.

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Gaussian Process Regression With Automatic Relevance ...

02/10/2019  Battery calendar aging prediction is of extreme importance for developing durable electric vehicles. This article derives machine learning-enabled calendar aging prediction for lithium-ion batteries. Specifically, the Gaussian process regression (GPR) technique is employed to capture the underlying mapping among capacity, storage temperature, and state-of-charge. By modifying the

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Prediction of Rubber Fiber Concrete Strength Using Extreme ...

28/01/2021  The training time of extreme learning and support vector machine is similar, and both are shorter than the BP neural network. Jian Tang et al. compared several concrete compressive strength prediction model methods based on extreme learning machines (ELMs) (Tang et al., 2014). The results show that the conventional ELMs algorithm has fast ...

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Using machine learning to predict extreme events in ...

07/01/2020  Using machine learning to predict extreme events in complex systems. View ORCID Profile Di Qi and Andrew J. Majda. a Department of Mathematics, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012; b Center for Atmosphere and Ocean Science, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012; See all Hide

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