scholarly journals SVM Parameter Optimization Based on Immune Memory Clone Strategy and Application in Bus Passenger Flow Counting

2012 ◽  
Vol 6-7 ◽  
pp. 694-699
Author(s):  
Fang Zhu ◽  
Jun Fang Wei
Keyword(s):  
Support Vector Machine ◽  
Optimization Method ◽  
Parameters Optimization ◽  
Immune Memory ◽  
Support Vector ◽  
Model Parameters ◽  
Passenger Flow ◽  
Parameters Selection ◽  
Svm Model ◽  
Selection Of

The performance of support vector machine (SVM) depends on the selection of model parameters, however, the selection of SVM model parameters more depends on the empirical value. According to the above deficiency, this paper proposed a parameters optimization method of support vector machine based on immune memory clone strategy (IMC). This method can solve the multi-peak model parameters selection problem better which is introduced by n-folded cross-verification. Tests on standard datasets show that this method has higher precision and faster optimization speed compared with other four methods. Then the proposed method was applied to bus passenger flow counting. The experimental results show that the method reposed in this paper obtains higher classification accuracy.

SVM Parameter Optimization based on Immune Memory Clone Strategy and Application in Bus Passenger Flow Counting

2012 ◽  
Vol 4 (4) ◽  
pp. 74-80
Author(s):  
Zhu Fang ◽  
Wei Junfang
Keyword(s):  
Optimization Method ◽  
Parameters Optimization ◽  
Immune Memory ◽  
Support Vector ◽  
Model Parameters ◽  
Passenger Flow ◽  
Parameters Selection ◽  
Svm Model ◽  
Peak Model ◽  
Selection Of

The performance of support vector mchine (SVM) depends on the selection of model parameters, however, the selection of SVM model parameters more depends on the empirical value. According to the above deficiency, this paper proposed a parameters optimization method of support vector machine based on immune memory clone strategy (IMC). This method can solve the multi-peak model parameters selection problem better which is introduced by n-folded cross-verification. Tests on standard datasets show that this method has higher precision and faster optimization speed compared with other four methods. Then the proposed method was applied to bus passenger flow counting. The experimental results show that the method reposed in this paper obtains higher classification accuracy.

A Novel Parameter Optimization Algorithm Based on Immune Memory Clone Strategy

2012 ◽  
Vol 4 (3) ◽  
pp. 102-108
Author(s):  
Zhu Fang ◽  
Wei Junfang
Keyword(s):  
Support Vector Machine ◽  
Optimization Method ◽  
Parameters Optimization ◽  
Immune Memory ◽  
Support Vector ◽  
Model Parameters ◽  
Parameters Selection ◽  
Svm Model ◽  
Peak Model ◽  
Selection Of

The performance of support vector machine (SVM) depends on the selection of model parameters, however, the selection of SVM model parameters more depends on the empirical value. According to the deficiency, this paper proposes a parameters optimization method of support vector machine based on immune memory clone strategy (IMC). This method can solve the multi-peak model parameters selection problem better which is introduced by n-folded cross-verification. Tests on standard datasets show that this method has higher precision and faster optimization speed compared with other four methods. The proposed method was applied to bus passenger flow counting. The experimental results show that the method reposed in this paper obtains higher classification accuracy.

Research on Parameters Optimization Algorithm in Support Vector Machine Based on Immune Memory Clone Strategy

2012 ◽  
Vol 241-244 ◽  
pp. 1618-1621
Author(s):  
Fang Zhu ◽  
Jun Fang Wei
Keyword(s):  
Support Vector Machine ◽  
Optimization Method ◽  
Parameters Optimization ◽  
Immune Memory ◽  
Support Vector ◽  
Model Parameters ◽  
Standard Data ◽  
Parameters Selection ◽  
Optimum Model ◽  
Selection Of

The performance of support vector machine (SVM) depends on the selection of model parameters, however, the selection of SVM model parameters more depends on the empirical value. According to the above deficiency, this paper proposed a parameters optimization method of support vector machine based on immune memory clone strategy (IMC). This method can solve the multi-peak model parameters selection problem better which is introduced by n-folded cross-verification and automatic acquire the optimum model parameters. Proved by the simulation results on standard data, this method has higher precision and faster optimization speed. In a word, it can be used as an effective and feasible SVM parameters optimization method.

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.

A Novel Hybrid Model for Solar Radiation Forecasting Using Support Vector Machine and Bee Colony Optimization Algorithm: Review and Case Study

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Mawloud Guermoui ◽  
Kacem Gairaa ◽  
John Boland ◽  
Toufik Arrif
Keyword(s):  
Support Vector Machine ◽  
Solar Radiation ◽  
Least Squares ◽  
Correlation Coefficient ◽  
Cuckoo Search ◽  
Measured Data ◽  
Parameters Optimization ◽  
Support Vector ◽  
Bee Colony ◽  
Svm Model

Abstract This article proposes a new hybrid least squares-support vector machine and artificial bee colony algorithm (ABC-LS-SVM) for multi-hour ahead forecasting of global solar radiation (GHI) data. The framework performs on training the least squares-support vector machine (LS-SVM) model by means of the ABC algorithm using the measured data. ABC is developed for free parameters optimization for the LS-SVM model in a search space so as to boost the forecasting performance. The developed ABC-LS-SVM approach is verified on an hourly scale on a database of five years of measurements. The measured data were collected from 2013 to 2017 at the Applied Research Unit for Renewable Energy (URAER) in Ghardaia, south of Algeria. Several combinations of input data have been tested to model the desired output. Forecasting results of 12 h ahead GHI with the ABC-LS-SVM model led to the root-mean-square error (RMSE) equal to 116.22 Wh/m2, Correlation coefficient r = 94.3%. With the classical LS-SVM, the RMSE error equals to 117.73 Wh/m2 and correlation coefficient r = 92.42%; for cuckoo search algorithm combined with LS-SVM, the RMSE = 116.89 Wh/m2 and r = 93.78%. The results achieved reveal that the proposed hybridization scheme provides a more accurate performance compared to cuckoo search-LS-SVM and the stand-alone LS-SVM.

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.

Hidden Defects Diagnosis Using Parameter Optimization

2012 ◽  
Vol 152-154 ◽  
pp. 1691-1697
Author(s):  
X. C. Wen ◽  
W.Y. Leong
Keyword(s):  
Support Vector Machine ◽  
Parameter Optimization ◽  
Great Influence ◽  
Optimization Procedure ◽  
Original Data ◽  
Component Analysis ◽  
Support Vector ◽  
Parameters Selection ◽  
Defects Classification ◽  
Selection Of

In this paper, the issue of composite defects diagnosis by applying the support vector machine (SVM) was addressed. The component analysis was performed initially to extract the features and to reduce the dimensionality of original data features. Kernel parameters selection of support vector machine which has great influence on the performance of defects classification has been discussed in this work. Precisely, we focus on wavelet transform to extract the feature from the original signals, adopt component analysis to do feature selection and apply support vector machine to classify the defects. This paper exploits the parameter optimization procedure to ensure the generalization ability of SVM. The result shows that multi-class SVM produces promising results and has the potential for use in fault diagnosis.

Sign in / Sign up

Export Citation Format

Share Document