A New Score Correlation Analysis Multi-class Support Vector Machine for Microarray

2007 ◽  
Author(s):  
Xiao-Lei Xia ◽  
Kang Li
Keyword(s):  
Support Vector Machine ◽  
Correlation Analysis ◽  
Support Vector

The Thermal Error Prediction of NC Machine Tool Based on LS-SVM and Grey Theory

2009 ◽  
Vol 16-19 ◽  
pp. 410-414 ◽  
Author(s):  
Chang Long Zhao ◽  
Yi Qiang Wang ◽  
Xue Song Guan
Keyword(s):  
Support Vector Machine ◽  
Correlation Analysis ◽  
Prediction Model ◽  
Machine Tool ◽  
Least Squares ◽  
Thermal Error ◽  
Support Vector ◽  
Error Prediction ◽  
Nc Machine Tool ◽  
Nc Machine

In this paper, a hybrid method of correlation analysis based on the gray theory and the least squares support vector machine is proposed to model the thermal error of spindle of NC machine tool and predict the thermal error. The gray correlation analysis is used to optimize the measuring points of spindle. The optimum measuring points and the measured thermal error of spindle are regarded as the data to be trained to build the thermal error prediction model based on the least squares support vector machine (LS-SVM). The results show that the thermal error prediction model based on LS-SVM of NC machine tool has advantages of high precision and good generalization performance. The prediction model can be used in real-time compensation of NC machine tool and can prove the process precision and reduce cost.

scholarly journals Crack Prediction Based on Wavelet Correlation Analysis Least Squares Support Vector Machine for Stone Cultural Relics

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Bao Liu ◽  
Fei Ye ◽  
Kun Mu ◽  
Jingting Wang ◽  
Jinyu Zhang
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Correlation Analysis ◽  
Environmental Monitoring ◽  
Least Squares ◽  
Multivariate Time Series ◽  
Shaanxi Province ◽  
Support Vector ◽  
Archaeological Heritage ◽  
Cultural Relics

Preventive protection of cultural relics is to make use of all the science and technology beneficial to the research and protection of archaeological heritage to predict the disease of cultural relics. The existing preventive cultural relics protection system has made some achievements in environmental monitoring, but the analysis and utilization of large data of cultural relics are still insufficient. In this paper, under the idea of multisource information fusion, a least squares support vector machine regression method based on multivariate time series wavelet correlation analysis is proposed to achieve accurate crack prediction of stone cultural relics. Firstly, the correlation of multivariate time series of stone cultural relics are quantitatively analyzed and the validity of characteristic variables of the crack is discriminated by wavelet correlation analysis; then, a least squares support vector machine prediction model is constructed based on the correlation obtained from the analysis; finally, the good performance of the method is verified by using the environmental monitoring data of the rock mass fracture in the North Qianfo Cliff of Dafo Temple in Binzhou City of Shaanxi Province. The experimental results show that the proposed method is more effective than the traditional backpropagation neural network, support vector machine, and relevance vector machine regression methods. This method is universal and easy to implement for multisource data prediction of nonmovable cultural relics diseases. It provides a scientific theoretical reference for the preventive protection of cultural relics.

scholarly journals Application of Network Security Event Correlation Analysis and Situation Evaluation Technology

2021 ◽  
pp. 207-214
Author(s):  
Yu Qing
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Network Security ◽  
Correlation Analysis ◽  
Situation Awareness ◽  
Evaluation Method ◽  
Particle Swarm ◽  
Support Vector ◽  
Situation Assessment ◽  
Swarm Optimization

Network security situational awareness can integrate all aspects of network security elements. Through correlation analysis, information fusion, situation prediction and other technologies to realize the intelligent analysis and comprehensive decision-making of complex information systems, network security situation awareness can improve the management efficiency and effect of complex networks. In order to solve the problem of parameter optimization of existing situation assessment methods, the parameters of SVM model are optimized based on Particle Swarm Optimization PSO algorithm. This paper presents a network security situation assessment method based on PSO and SVM. Using this algorithm can get a better balance between time-consuming and improving accuracy. At the same time, the index weight is determined according to grey correlation analysis, and the training samples are input to support vector machine for training. In this paper, the improved particle swarm optimization algorithm is used to optimize the parameters of support vector machine to improve the effect of situation assessment. Simulation test results show that the evaluation method improves the effectiveness and accuracy of situation assessment.

A Spectrum Sensing Based on Support Vector Machine Algorithm in the Building Indoors Environment

2014 ◽  
Vol 945-949 ◽  
pp. 2297-2300 ◽  
Author(s):  
Xing Hua Xia ◽  
Fang Jun Luan ◽  
Meng Xin Li
Keyword(s):  
Support Vector Machine ◽  
Correlation Analysis ◽  
Spectrum Sensing ◽  
Indoor Environment ◽  
Signal To Noise Ratio ◽  
Primary User ◽  
Support Vector ◽  
Characteristic Parameters ◽  
Spectral Correlation ◽  
Low Snr

Spectrum sensing performance of building indoor environment has been the focus of attention and research in low signal-to-noise ratio. In this paper, a primary users sensing approach to signal classification combining spectral correlation analysis and support vector machine (SVM) is introduced. Three spectral coherence characteristic parameters are chosen via spectral correlation analysis. By utilizing a nonlinear SVM, primary user signal has been detected. Simulations indicate that the overall success rate is above 90.2% when SNR is equal to-5dB and 80.1% in-15dB. Compared to the existing methods including the classifiers based on MME and ANN, the proposed approach is more effective in the case of low SNR and limited training numbers. The results show that the validity and superiority of the proposed algorithm in building indoor environment.

An Online LS-SVM Prediction Model of Building Space Cooling Load

2011 ◽  
Vol 354-355 ◽  
pp. 789-793 ◽  
Author(s):  
Shuang Hua Cao ◽  
Jiang Tao Zhang ◽  
Fang Liu ◽  
Min Si Li
Keyword(s):  
Support Vector Machine ◽  
Correlation Analysis ◽  
Simulation Software ◽  
Office Building ◽  
Support Vector ◽  
Cooling Load ◽  
Load Prediction ◽  
Space Cooling ◽  
Svm Model ◽  
Simulation Results

Building space cooling load affected by lots of factors,was transient, multi-dimensional and highly interactive. A model of online least squares support vector machine (LS-SVM) was established to forecast the space cooling load of building, and correlation analysis was used to find out the main influencing factors, such as, dry-bulb temperature, solar irradiance, and so on. As an example, the hourly space cooling load of an office building in shanghai was investigated. The hourly space cooling load was firstly calculated by the simulation software of DEST, and then, an online LS-SVM model was presented to forecast the load. The simulation results showed that the online LS-SVM model was effective for space cooling load prediction.

A Model for Predicting the Wear Degree of Electrode Tip

2014 ◽  
Vol 574 ◽  
pp. 292-297
Author(s):  
Jin Zhang ◽  
Peng Xian Zhang ◽  
Xiang Jian Xu
Keyword(s):  
Support Vector Machine ◽  
Correlation Analysis ◽  
Digital Image ◽  
Model Prediction ◽  
Support Vector ◽  
Laser Measurement ◽  
Characteristic Parameters ◽  
Laser Measurement System ◽  
Image Characteristic ◽  
Test Result

A new method is put forward to predicting the degree of electrode tip wear based on a laser measurement and digital image of the surface joint indentation. First, in order to monitoring the degree of electrode tip wear, the decline altitudes of sphere ΔH that can indicate variation of electrode tip shape are measured by means of the laser measurement system. Second, through the correlation analysis between the parameters S0, S1, S, K1 reflecting digital image characteristic of joint indentation and the decline altitudes of sphere ΔH, S0, S, K1 are extracted as characteristic parameters of monitoring electrode tip wear. At last, a model of support vector machine (SVM) for predicting the degree of electrode tip wear is established between the parameters S0, S, K1 as the input vector and ΔH as the target vector. Test result shows, the correlation coefficient between model prediction and actual measured values are 0.9907. The prediction model can realize estimating the degree of electrode tip wear.

scholarly journals Improvement of the Classification Accuracy of Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces by Combining L1-MCCA with SVM

2021 ◽  
Vol 11 (23) ◽  
pp. 11453
Author(s):  
Yuhang Gao ◽  
Juanning Si ◽  
Sijin Wu ◽  
Weixian Li ◽  
Hao Liu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Steady State ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Classification Accuracy ◽  
Canonical Correlation ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Support Vector ◽  
Visual Evoked

Canonical correlation analysis (CCA) has been used for the steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) for a long time. However, the reference signal of CCA is relatively simple and lacks subject-specific information. Moreover, over-fitting may occur when a short time window (TW) length was used in CCA. In this article, an optimized L1-regularized multiway canonical correlation analysis (L1-MCCA) is combined with a support vector machine (SVM) to overcome the aforementioned shortcomings in CCA. The correlation coefficients obtained by L1-MCCA were transferred into a particle-swarm-optimization (PSO)-optimized support vector machine (SVM) classifier to improve the classification accuracy. The performance of the proposed method was evaluated and compared with the traditional CCA and power spectral density (PSD) methods. The results showed that the accuracy of the L1-MCCA-PSO-SVM was 96.36% and 98.18% respectively when the TW lengths were 2 s and 6 s. This accuracy is higher than that of the traditional CCA and PSD methods.

Sign in / Sign up

Export Citation Format

Share Document