scholarly journals Bearing Fault Diagnosis Using a Support Vector Machine Optimized by an Improved Ant Lion Optimizer

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Dalian Yang ◽  
Jingjing Miao ◽  
Fanyu Zhang ◽  
Jie Tao ◽  
Guangbin Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Model Parameters ◽  
Bearing Faults ◽  
Ring Fault ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Svm Model

Bearing is an important mechanical component that easily fails in a bad working environment. Support vector machines can be used to diagnose bearing faults; however, the recognition ability of the model is greatly affected by the kernel function and its parameters. Unfortunately, optimal parameters are difficult to select. To address these limitations, an escape mechanism and adaptive convergence conditions were introduced to the ALO algorithm. As a result, the EALO method was proposed and has been applied to the more accurate selection of SVM model parameters. To assess the model, the vibration acceleration signals of normal, inner ring fault, outer ring fault, and ball fault bearings were collected at different rotation speeds (1500 r/min, 1800 r/min, 2100 r/min, and 2400 r/min). The vibration signals were decomposed using the variational mode decomposition (VMD) method. The features were extracted through the kernel function to fuse the energy value of each VMD component. In these experiments, the two most important parameters for the support vector machine—the Gaussian kernel parameter σ and the penalty factor C—were optimized using the EALO algorithm, ALO algorithm, genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. The performance of these four methods to optimize the two parameters was then compared and analyzed, with the EALO method having the best performance. The recognition rates for bearing faults under different tested rotation speeds were improved when the SVM model parameters optimized by the EALO were used.

Kernel Parameter Selection for SVM Classification

2011 ◽  
pp. 44-55
Author(s):  
Manju Bala ◽  
R. K. Agrawal
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Classification Accuracy ◽  
Parameter Selection ◽  
Kernel Functions ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Svm Classification ◽  
Kernel Parameter ◽  
Benchmark Datasets

The choice of kernel function and its parameter is very important for better performance of support vector machine. In this chapter, the authors proposed few new kernel functions which satisfy the Mercer’s conditions and a robust algorithm to automatically determine the suitable kernel function and its parameters based on AdaBoost to improve the performance of support vector machine. The performance of proposed algorithm is evaluated on several benchmark datasets from UCI repository. The experimental results for different datasets show that the Gaussian kernel is not always the best choice to achieve high generalization of support vector machine classifier. However, with the proper choice of kernel function and its parameters using proposed algorithm, it is possible to achieve maximum classification accuracy for all datasets.

SVM MODELS FOR DIAGNOSING BALANCE PROBLEMS USING STATISTICAL FEATURES OF THE MTC SIGNAL

2008 ◽  
Vol 07 (03) ◽  
pp. 317-331 ◽  
Author(s):  
DANIEL T. H. LAI ◽  
REZAUL BEGG ◽  
MARIMUTHU PALANISWAMI
Keyword(s):  
Feature Selection Method ◽  
Gaussian Kernel ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Model Parameters ◽  
Statistical Features ◽  
Gait Patterns ◽  
Healthy Elderly ◽  
Svm Model ◽  
Balance Problems

Trip-related falls are a major problem in the elderly population and research in the area has received much attention recently. The focus has been on devising ways of identifying individuals at risk of sustaining such falls. The main aim of this work is to explore the effectiveness of models based on Support Vector Machines (SVMs) for the automated recognition of gait patterns that exhibit falling behavior. Minimum toe clearance (MTC) during continuous walking on a treadmill was recorded on 10 healthy elderly and 10 elderly with balance problems and with a history of tripping falls. Statistical features obtained from MTC histograms were used as inputs to the SVM model to classify between the healthy and balance-impaired subjects. The leave-one-out technique was utilized for training the SVM model in order to find the optimal model parameters. Tests were conducted with various kernels (linear, Gaussian and polynomial) and with a change in the regularization parameter, C, in an effort to identify the optimum model for this gait data. The receiver operating characteristic (ROC) plots of sensitivity and specificity were further used to evaluate the diagnostic performance of the model. The maximum accuracy was found to be 90% using a Gaussian kernel with σ2 = 10 and the maximum ROC area 0.98 (80% sensitivity and 100% specificity), when all statistical features were used by the SVM models to diagnose gait patterns of healthy and balance-impaired individuals. This accuracy was further improved by using a feature selection method in order to reduce the effect of redundant features. It was found that two features (standard deviation and maximum value) were adequate to give an improved accuracy of 95% (90% sensitivity and 100% specificity) using a polynomial kernel of degree 2. These preliminary results are encouraging and could be useful not only for diagnostic applications but also for evaluating improvements in gait function in the clinical/rehabilitation contexts.

Study on the Quantitative Method of Oversaturated Intersection

2014 ◽  
Vol 587-589 ◽  
pp. 2100-2104
Author(s):  
Qin Liu ◽  
Jian Min Xu ◽  
Kai Lu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Travel Speed ◽  
Urban Traffic ◽  
Support Vector ◽  
Model Parameters ◽  
Guangzhou City ◽  
Traffic System ◽  
Traffic Conditions ◽  
Svm Model

Oversaturation in the modern urban traffic often happens. In order to describe the degree of oversaturation, the indexes of intersection oversaturation degree are put forward include dissipation time, stranded queue, overflow queue and travel speed. On the basis of selected indexes, the genetic algorithm support vector machine (GA-SVM) model was proposed to quantify the degree of oversaturation. In this method the genetic algorithm is used to select the model parameters. The GA-SVM model built is used to quantify the degree of oversaturation. Combining with the volume of intersections in Guangzhou city the method is calculated and simulated through programming. The simulation results show that GA-SVM method is effective and the accuracy of GA-SVM is higher than support vector machine (SVM).This method provides a theoretical basis for the analysis of traffic system under over-saturated traffic conditions.

scholarly journals Risk prediction of early diabetes mellitus based on combination model

2021 ◽  
Vol 336 ◽  
pp. 07018
Author(s):  
Haoxin Tang ◽  
Yi Zhang ◽  
Baolin Xiang ◽  
Mingkun Liu ◽  
Junming Hu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Model Performance ◽  
Support Vector ◽  
Combined Model ◽  
Early Diabetes ◽  
Verification Algorithm ◽  
Bayes Model ◽  
Penalty Factor ◽  
Svm Model ◽  
Sensitivity Specificity

Aiming at the current low pre-diabetes detection rate, this paper proposes a PSO-SVM model to assist doctors in identifying the risk of patients with pre-diabetes. The paper uses the Support Vector Machine as the verification algorithm, takes the radial basis kernel as the kernel function, uses the adaptive Particle Swarm Optimization algorithm to optimize the penalty factor and kernel parameters of the Support Vector Machine, and establishes a PSO-SVM model, finally compares the model with Neural Network, Logistic Regression, and Naive Bayes model, and use Sensitivity, Specificity indicators and ROC curve to evaluate model performance. Empirical analysis proves that the combined model proposed in this paper can effectively identify the risk of patients with prediabetes.

Hyper-sphere support vector machine based on collaborative knowledge mining and SMO optimization in power load forecasting

2020 ◽  
Author(s):  
Resheng PAN ◽  
Hui Li ◽  
Zhidong WANG ◽  
Dong PENG ◽  
Lang ZHAO ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Load Forecasting ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Knowledge Mining ◽  
Power Load ◽  
Svm Model ◽  
Sample Data ◽  
Power Load Forecasting ◽  
Collaborative Knowledge

Abstract Background Due to the influence of power market reform policies, the conversion of power loads has become more and more complicated. The current load forecasting methods have long calculation times and inaccurate volatility load forecasting. The difficulty of forecasting is becoming greater and more accurate. It becomes very important to predict the electrical load. Under this background, this paper proposes the application methods of collaborative knowledge mining and SMO in solving prediction models based on hyperball support vector machine (CKM / SMO-SVM). Methods This study first analyzes the impact of historical data on samples and different parameters. The prediction of power load, sample data and various parameters have a significant impact on the prediction results. Secondly, applying weak entropy theory for collaborative knowledge mining, preprocessing sample data and historical information. Third, a short-term power load forecasting system based on the hypersphere support vector machine model is established and the problem is solved by SMO. Finally, the SVM model and BP model are selected for prediction to verify the new model. Results Our research proves that the rms relative error of the CKM / SMO-SVM model is only 2.32%, which is 0.67% and 1.56% lower than the SVM and BP models, respectively, and the optimization speed is faster. Conclusions The model proposed in this paper utilizes Hyper-sphere SVM which is suitable for Gaussian kernel function to achieve faster and more accurate load forecasting, which can provide more accurate services for energy spot transactions and energy scheduling plans.

Signal Classification for Pipeline Security Threat Event Based on Optimized Support Vector Machine

2016 ◽  
Vol 693 ◽  
pp. 1428-1435
Author(s):  
Dong Jie Tan ◽  
Hong Zhang ◽  
Lu Liu
Keyword(s):  
Support Vector Machine ◽  
Fitness Function ◽  
Engineering Application ◽  
Warning System ◽  
Vibration Signal ◽  
Support Vector ◽  
Security Threats ◽  
Security Threat ◽  
Penalty Factor ◽  
Kernel Parameter

The way of efficiently classifying the manual digging, machine excavation, vehicle passing and other pipeline security threats, is an imperative problem for optical fiber pipeline security warning system. To solve this problem, a security threats classification method based on optimized support vector machine is proposed. In this method, after feature extraction based on wavelet to the original vibration signal, the artificial bee colony algorithm is introduced to optimize the penalty factor and kernel parameter of support vector machine under specified fitness function, and the optimized support vector machine is used to classify the pipeline security threats. To testify the performance of the proposed method, the experiment based on UCI feature datasets and actual vibration signal are made. Comparing with the support vector machine optimized by other algorithms, higher classification accuracy and less time consumption is achieved by the proposed method. Therefore, the effectiveness and the engineering application value of this proposed method is testified.

Stock Index Prediction and Analysis Based on GASA-SVM

2013 ◽  
Vol 798-799 ◽  
pp. 842-845
Author(s):  
Li Zhe Ma
Keyword(s):  
Support Vector Machine ◽  
Simulated Annealing ◽  
Prediction Model ◽  
Simulated Annealing Algorithm ◽  
Optimal Parameter ◽  
Stock Index ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Kernel Parameter ◽  
Annealing Algorithm

In order to improve the prediction accuracy of stock index, eliminate of the blindness of parameters selection for support vector machine, a stock index prediction method combined the genetic simulated annealing algorithm (GASA) which integrated the parallel search of genetic algorithm with the probabilistic sudden jumping characteristics of simulated annealing algorithm, with support vector machine (SVM) is proposed. Using daily data of Shanghai stock index opening quotation which is normalization processed, the stock index prediction model based on GASA-SVM is established. Optimal parameter error penalty parameter c=1 and Gaussian kernel parameter g=1.625 are obtained. Compared the result with GA-SVM prediction model, the comparative analysis shows that GASA-SVM(MSE= 0.000191111) model prediction capabilities are superior to GA-SVM(MSE=0.000018825) prediction model. It can provide valuable references for the investors.

scholarly journals Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Davies Segera ◽  
Mwangi Mbuthia ◽  
Abraham Nyete
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm ◽  
Gaussian Kernel ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Linear Kernel ◽  
Penalty Factor ◽  
Multiclass Support Vector Machine ◽  
Factor C ◽  
Hybrid Kernel

Determining an optimal decision model is an important but difficult combinatorial task in imbalanced microarray-based cancer classification. Though the multiclass support vector machine (MCSVM) has already made an important contribution in this field, its performance solely depends on three aspects: the penalty factor C, the type of kernel, and its parameters. To improve the performance of this classifier in microarray-based cancer analysis, this paper proposes PSO-PCA-LGP-MCSVM model that is based on particle swarm optimization (PSO), principal component analysis (PCA), and multiclass support vector machine (MCSVM). The MCSVM is based on a hybrid kernel, i.e., linear-Gaussian-polynomial (LGP) that combines the advantages of three standard kernels (linear, Gaussian, and polynomial) in a novel manner, where the linear kernel is linearly combined with the Gaussian kernel embedding the polynomial kernel. Further, this paper proves and makes sure that the LGP kernel confirms the features of a valid kernel. In order to reveal the effectiveness of our model, several experiments were conducted and the obtained results compared between our model and other three single kernel-based models, namely, PSO-PCA-L-MCSVM (utilizing a linear kernel), PSO-PCA-G-MCSVM (utilizing a Gaussian kernel), and PSO-PCA-P-MCSVM (utilizing a polynomial kernel). In comparison, two dual and two multiclass imbalanced standard microarray datasets were used. Experimental results in terms of three extended assessment metrics (F-score, G-mean, and Accuracy) reveal the superior global feature extraction, prediction, and learning abilities of this model against three single kernel-based models.

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