A novel hybrid kernel function relevance vector machine for multi-task motor imagery EEG classification

2020 ◽  
Vol 60 ◽  
pp. 101991 ◽  
Author(s):  
Enzeng Dong ◽  
Kairui Zhou ◽  
Jigang Tong ◽  
Shengzhi Du
Keyword(s):  
Motor Imagery ◽  
Kernel Function ◽  
Relevance Vector Machine ◽  
Eeg Classification ◽  
Hybrid Kernel Function ◽  
Hybrid Kernel

Research on the Fouling Prediction Based on Hybrid Kernel Function Relevance Vector Machine

2011 ◽  
Vol 204-210 ◽  
pp. 31-35
Author(s):  
Ling Fang Sun ◽  
Hong Gang Xie ◽  
Li Hong Qiao
Keyword(s):  
Kernel Function ◽  
Relevance Vector Machine ◽  
Economic Benefits ◽  
Training Data ◽  
Gaussian Kernel ◽  
Polynomial Kernel ◽  
Simulation Research ◽  
Dynamic Simulator ◽  
Hybrid Kernel Function ◽  
Hybrid Kernel

The research on the fouling prediction of heat exchanger is significantly to improve operational efficiency and economic benefits of the plants. Based on the relevance vector machine with Gaussian kernel function, polynomial kernel function and hybrid kernel function, simulation research on the fouling prediction was introduced. We construct a six-inputs and one-output network model according to the fouling monitor principle and parameters with MATLAB, all training data came from the Automatic Dynamic Simulator of Fouling and input the network after normalized processing and reclassification. Simulations show that the root mean square error of fouling prediction with hybrid kernel function is less than simple kernel function, and has the better prediction precision.

Fuzzy complementary entropy using hybrid-kernel function and its unsupervised attribute reduction

2021 ◽  
Vol 231 ◽  
pp. 107398
Author(s):  
Zhong Yuan ◽  
Hongmei Chen ◽  
Xiaoling Yang ◽  
Tianrui Li ◽  
Keyu Liu
Keyword(s):  
Kernel Function ◽  
Attribute Reduction ◽  
Hybrid Kernel Function ◽  
Hybrid Kernel

Stock Price Index Prediction Based on Improved SVM

2011 ◽  
Vol 267 ◽  
pp. 468-471
Author(s):  
Jin Yan Shi ◽  
Xue Li ◽  
Yan Xi Li
Keyword(s):  
Kernel Function ◽  
Stock Price ◽  
Price Index ◽  
Kernel Functions ◽  
Classification Model ◽  
Support Vector ◽  
Theoretical Advantage ◽  
Stock Price Index ◽  
Hybrid Kernel Function ◽  
Hybrid Kernel

Accurate stock price predicting is a key problem to the financial field. Comparing with the traditional stock price predicting models such as GARCH models and neural networks, the theoretical advantage of applying support vector machine (SVM) to stock price predicting highly depends on solving the problem of kernel function construction and parameter optimization. For the effect of the kernel function in the SVM classification model, a hybrid kernel function is presented. In order to optimize and adjust the important parameters during the process of building the hybrid kernel function, an improved particle swarm optimization which has better global search ability is used. Experimental results about stock price index predicting show that this method has higher prediction accuracy compared with the traditional kernel functions.

Multi-User Detection Based on Improved KICA with Bat Algorithm

2013 ◽  
Vol 336-338 ◽  
pp. 1867-1870
Author(s):  
Xiao Zhi Liu ◽  
Jing Li
Keyword(s):  
Independent Component Analysis ◽  
Kernel Function ◽  
Bit Error Rate ◽  
Bat Algorithm ◽  
Kernel Functions ◽  
Independent Component ◽  
Kernel Independent Component Analysis ◽  
Simulation Results ◽  
Hybrid Kernel Function ◽  
Hybrid Kernel

In this paper, an improved kernel independent component analysis (KICA) algorithm is proposed for multi-user detection (MUD). In this algorithm, a new hybrid kernel function is adopted. In addition, the bat algorithm is applied to the optimizing process of independent component separation. Simulation results show that the new hybrid kernel function performs better in MUD than other kernel functions, and the improved KICA with bat algorithm has the smallest bit error rate (BER) when compared with classical FastICA and KICA algorithms.

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