Application of support vector machine and ant colony algorithm in optimization of coal ash fusion temperature

Support vector machine (SVM) is suitable for the classification problem which is of small sample, nonlinear, high dimension. SVM in data preprocessing phase, often use genetic algorithm for feature extraction, although it can improve the accuracy of classification. But in feature extraction stage the weak directivity of genetic algorithm impact the time and accuracy of the classification. The ant colony algorithm is used in genetic algorithm selection stage, which is better for the data pretreatment, so as to improve the classification speed and accuracy. The experiment in the KDD99 data set shows that this method is feasible.

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Natural Image Classification Based on Improved Support Vector Machine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.58-60.2387 ◽

2011 ◽

Vol 58-60 ◽

pp. 2387-2391

Author(s):

Ying Jian Qi ◽

Zhi Wei Ou ◽

Bin Zhang ◽

Ting Zhan Liu ◽

Ying Li

Keyword(s):

Support Vector Machine ◽

Image Classification ◽

Ant Colony Algorithm ◽

Image Representation ◽

Image Features ◽

Ant Colony ◽

Function Parameter ◽

Support Vector ◽

Natural Image ◽

Improved Support Vector Machine

Local image representation based natural image classification is an important task. SIFT descriptors and bag-of-visterm (BOV)method have achieved very good results. Many studies focused on improving the representation of the image, and then use the support vector machine to classify and identify the image category. However, due to support vector machine its own characteristics, it shows inflexible and slower convergence rate for large samples，with the selection of parameters influencing the results for the algorithm very much. Therefore, this paper will use the improved support vector machine algorithm be based on ant colony algorithm in classification step. The method adopt dense SIFT descriptors to describe image features and then use two levels BOV method to obtain the image representation. In recognition step, we use the support vector machine as a classifier but ant colony optimization method is used to selects kernel function parameter and soft margin constant C penalty parameter. Experiment results show that this solution determined the parameter automatically without trial and error and improved performance on natural image classification tasks.

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Research on SVM Cell Concentration Prediction of Erythromycin Fermentation Process Based on Ant Colony Optimization

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.734-737.2998 ◽

2013 ◽

Vol 734-737 ◽

pp. 2998-3002

Author(s):

Li Tian ◽

Qiang Qiang Wang ◽

An Zhao Cao

Keyword(s):

Support Vector Machine ◽

Fermentation Process ◽

Ant Colony Algorithm ◽

Cell Concentration ◽

Ant Colony ◽

Support Vector ◽

Nonlinear Complexity ◽

Svm Model ◽

Concentration Prediction

Due to disadvantages of nonlinear, complexity and uncertainty of fermentation process, a research on cell concentration prediction of erythromycin fermentation process was carried out. Combining the optimization ability of ant colony algorithm and the regression ability of support vector machine, an ACO-SVM model is built. Case study shows that, the model is more accurate and more effective for the cell concentration prediction than ANN and SVM model.

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Fault diagnosis of gearbox based on ant colony algorithm optimized support vector machine

Journal of Physics Conference Series ◽

10.1088/1742-6596/2137/1/012068 ◽

2021 ◽

Vol 2137 (1) ◽

pp. 012068

Author(s):

Shilin Sun ◽

Renxiang Lu

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Ant Colony Algorithm ◽

Feature Vector ◽

Vibration Signal ◽

Ant Colony ◽

Fault Classification ◽

Function Parameter ◽

Support Vector ◽

Factor C

Abstract The kernel function parameter g and penalty factor c in Support Vector Machine (SVM) will have an important impact on the fault classification and performance of the support vector machine. Based on this, a fault analysis and diagnosis model using ant colony algorithm to optimize support vector machine is proposed to improve the accuracy of gearbox fault diagnosis. First, the collected original vibration signal is decomposed by EEMD to obtain the modal function component IMF, and then the energy entropy of the IMF component is calculated as the feature vector of the original vibration signal. Finally, the feature vector is input to the support vector optimized by the ant colony algorithm identify and classify in the machine, and finally get the diagnosis result. Comparing ACO-SVM with SVM, the experimental results prove that the ACO-SVM model has a higher fault diagnosis rate, stronger optimization ability, and faster convergence speed.

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