Research on Intelligent Optimization Method of Grid Communication Server Based on Support Vector Machine

2021 ◽  
pp. 323-331
Author(s):  
Xiaohui Zhu ◽  
Lu Ji ◽  
Huijing Bi ◽  
Xiaobo Zhao
Keyword(s):  
Support Vector Machine ◽  
Optimization Method ◽  
Support Vector ◽  
Intelligent Optimization ◽  
Communication Server

Fault Diagnosis Method of Mine Motor Based on Support Vector Machine

2019 ◽  
Vol 13 ◽  
Author(s):  
Yan Zhang ◽  
Ren Sheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimization Method ◽  
Support Vector ◽  
Optimal Parameters ◽  
Instantaneous Power ◽  
Practical Usefulness ◽  
Sample Data ◽  
Diagnosis Method ◽  
Fault Characteristic

Background: In order to improve the efficiency of fault treatment of mining motor, the method of model construction is used to construct the type of kernel function based on the principle of vector machine classification and the optimization method of parameters. Methodology: One-to-many algorithm is used to establish two kinds of support vector machine models for fault diagnosis of motor rotor of crusher. One of them is to obtain the optimal parameters C and g based on the input samples of the instantaneous power fault characteristic data of some motor rotors which have not been processed by rough sets. Patents on machine learning have also shows their practical usefulness in the selction of the feature for fault detection. Results: The results show that the instantaneous power fault feature extracted from the rotor of the crusher motor is obtained by the cross validation method of grid search k-weights (where k is 3) and the final data of the applied Gauss radial basis penalty parameter C and the nuclear parameter g are obtained. Conclusion: The model established by the optimal parameters is used to classify and diagnose the sample of instantaneous power fault characteristic measurement of motor rotor. Therefore, the classification accuracy of the sample data processed by rough set is higher.

Performance Prediction by an SVM with a Firefly Optimization Method

2020 ◽  
pp. 2150017
Author(s):  
Yongquan Yan
Keyword(s):  
Support Vector Machine ◽  
Economic Loss ◽  
Optimization Method ◽  
Parameter Selection ◽  
Support Vector ◽  
Software Aging ◽  
Memory Prediction ◽  
Firefly Optimization ◽  
Aging Phenomena ◽  
Selection Of

Since software system is becoming more and more complex than before, performance degradation and even abrupt download, which are called software aging phenomena, bring about a great deal of economic loss. To counter these problems, some methods are used. Support vector machine is an effective method to tackle software aging problems, but its performance is influenced by the selection of hyper-parameters. A method is proposed to optimize the hyper-parameter selection of support vector machine in this work. The proposed method which is used as a training algorithm to optimize the parameter selection of support vector machine, utilizes the global exploration power of firefly method to achieve faster convergence and also a better accuracy. In the experiment, we use two metrics to test the effect of the proposed method. The results indicate that the presented method owns the highest accuracy in both the available memory prediction and heap memory prediction of Web server for software aging predictions.

Support Vector Machine Optimization Based on Bacterial Foraging Algorithm and Applied in Fault Diagnosis

2011 ◽  
Vol 216 ◽  
pp. 153-157
Author(s):  
D.L. Yang ◽  
Xue Jun Li ◽  
K. Wang ◽  
Ling Li Jiang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Parameter Optimization ◽  
Rolling Bearing ◽  
Optimization Method ◽  
Parameters Optimization ◽  
Support Vector ◽  
Bearing Fault Diagnosis ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm

The parameter optimization is the key to study of support vector machine (SVM). With strong global search capability of bacterial foraging algorithm(BFA), the optimization method—support vector machine parameters optimization based on bacterial foraging algorithm was proposed, which can achieve the dynamic optimization of the parametersCandγ,and overcomes the problem of inefficiency for selecting reasonable parameters according to the experience in the traditional fault diagnosis. Compared with other methods, the BFA is simpler and easier for programming, and the optimization SVM model become smaller. The rolling bearing fault diagnosis results show that bacterial foraging algorithm is suitable for support vector machine parameter optimization.

scholarly journals Differential Evolution and Multiclass Support Vector Machine for Alzheimer’s Classification

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Jhansi Rani Kaka ◽  
K. Satya Prasad
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Differential Evolution ◽  
Optimization Method ◽  
Support Vector ◽  
Local Optima ◽  
Whale Optimization ◽  
Multiclass Support Vector Machine ◽  
Poor Convergence

Early diagnosis of Alzheimer’s helps a doctor to decide the treatment for the patient based on the stages. The existing methods involve applying the deep learning methods for Alzheimer’s classification and have the limitations of overfitting problems. Some researchers were involved in applying the feature selection based on the optimization method, having limitations of easily trapping into local optima and poor convergence. In this research, Differential Evolution-Multiclass Support Vector Machine (DE-MSVM) is proposed to increase the performance of Alzheimer’s classification. The image normalization method is applied to enhance the quality of the image and represent the features effectively. The AlexNet model is applied to the normalized images to extract the features and also applied for feature selection. The Differential Evolution method applies Pareto Optimal Front for nondominated feature selection. This helps to select the feature that represents the characteristics of the input images. The selected features are applied in the MSVM method to represent in high dimension and classify Alzheimer’s. The DE-MSVM method has accuracy of 98.13% in the axial slice, and the existing whale optimization with MSVM has 95.23% accuracy.

scholarly journals An Optimized Method of Wind Speed Prediction with Support Vector Machine and Genetic Algorithm

2021 ◽  
Vol 15 ◽  
pp. 212-217
Author(s):  
Guan-fa Li ◽  
Wen-sheng Zhu
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Support Vector Machine ◽  
Wind Speed ◽  
Power System ◽  
Prediction Models ◽  
Optimization Method ◽  
Support Vector ◽  
Wind Speed Prediction ◽  
Speed Prediction

Due to the randomness of wind speed and direction, the output power of wind turbine also has randomness. After large-scale wind power integration, it will bring a lot of adverse effects on the power quality of the power system, and also bring difficulties to the formulation of power system dispatching plan. In order to improve the prediction accuracy, an optimized method of wind speed prediction with support vector machine and genetic algorithm is put forward. Compared with other optimization methods, the simulation results show that the optimized genetic algorithm not only has good convergence speed, but also can find more suitable parameters for data samples. When the data is updated according to time series, the optimization range of vaccine and parameters is adaptively adjusted and updated. Therefore, as a new optimization method, the optimization method has certain theoretical significance and practical application value, and can be applied to other time series prediction models.

Image Steganalysis in High-Dimensional Feature Spaces with Proximal Support Vector Machine

2019 ◽  
Vol 11 (1) ◽  
pp. 78-89 ◽  
Author(s):  
Ping Zhong ◽  
Mengdi Li ◽  
Kai Mu ◽  
Juan Wen ◽  
Yiming Xue
Keyword(s):  
Support Vector Machine ◽  
Ridge Regression ◽  
Optimization Method ◽  
Least Square ◽  
Computational Time ◽  
Support Vector ◽  
Image Steganalysis ◽  
Linear Discriminant ◽  
Feature Sets ◽  
Proximal Support Vector Machine

This article presents the linear Proximal Support Vector Machine (PSVM) to the image steganalysis, and further generates a very efficient method called PSVM-LSMR through implementing PSVM by the state-of-the-art optimization method Least Square Minimum-Residual (LSMR). Also, motivated by extreme learning machine (ELM), a nonlinear algorithm PSVM-ELM is proposed for the image steganalysis. It is shown by the experiments with the wide stego schemes and rich steganalysis feature sets in both the spatial and JPEG domains that the PSVM can achieve comparable performance with Fisher Linear Discriminant (FLD) and ridge regression, and its computational time is far more less than that of them on large feature sets. The PSVM-LSMR is comparable to Ridge Regression implemented by LSMR (RR-LSMR), and both of them require the least computational time among all the competitions when dealing with medium or large feature sets. The nonlinear PSVM-ELM performs comparably or even better than FLD and ridge regression for the spatial domain steganographic schemes, and its computational time is apparently less than that of them on large feature sets.

Prediction of Periventricular Leukomalacia Occurrence in Neonates Using a Novel Support Vector Machine Classifier Optimization Method

2015 ◽  
Author(s):  
Dieter Bender ◽  
Ali Jalali ◽  
Daniel J. Licht ◽  
C. Nataraj
Keyword(s):  
Support Vector Machine ◽  
Periventricular Leukomalacia ◽  
Optimization Method ◽  
Computational Time ◽  
Support Vector ◽  
Svm Classifier ◽  
Learning Stage ◽  
Svm Algorithm ◽  
Initial Idea ◽  
Leave One Out

Prior work has documented that Support Vector Machine (SVM) classifiers can be powerful tools in predicting clinical outcomes of complex diseases such as Periventricular Leukomalacia (PVL). Our previous study showed that SVM performance can be improved significantly by optimizing the supervised training set used during the learning stage of the overall SVM algorithm. This study fully develops the initial idea using the reliable Leave-One-Out Cross-validation (LOOCV) technique. The work presented in this paper confirms previous results and improves the performance of the SVM even further. In addition, using the LOOCV technique, the computational time is decreased and the structure of the algorithm simplified, making this framework more feasible. Furthermore, we evaluate the performance of the resulting optimized SVM classifier on an unseen set of data. This demonstrates that the developed SVM algorithm outperforms normal SVM type classifiers without any loss of generalization.

scholarly journals Applicability of ε-Support Vector Machine and Artificial Neural Network for Flood Forecasting in Humid, Semi-Humid and Semi-Arid Basins in China

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 85 ◽  
Author(s):  
Thabo Michael Bafitlhile ◽  
Zhijia Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Optimization Method ◽  
Evolutionary Strategy ◽  
Support Vector ◽  
Climatic System ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Semi Arid

The aim of this study was to develop hydrological models that can represent different geo-climatic system, namely: humid, semi-humid and semi-arid systems, in China. Humid and semi-humid areas suffer from frequent flood events, whereas semi-arid areas suffer from flash floods because of urbanization and climate change, which contribute to an increase in runoff. This study applied ɛ-Support Vector Machine (ε-SVM) and artificial neural network (ANN) for the simulation and forecasting streamflow of three different catchments. The Evolutionary Strategy (ES) optimization method was used to optimize the ANN and SVM sensitive parameters. The relative performance of the two models was compared, and the results indicate that both models performed well for humid and semi-humid systems, and SVM generally perform better than ANN in the streamflow simulation of all catchments.

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