Application of support vector machine and quantum genetic algorithm in infrared target recognition

2010 ◽  
Author(s):  
Hongliang Wang ◽  
Yangwen Huang ◽  
Haifei Ding
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Target Recognition ◽  
Support Vector ◽  
Quantum Genetic Algorithm

Structural Damage Detection Based on Fuzzy LS-SVM Integrated Quantum Genetic Algorithm

2010 ◽  
Vol 20-23 ◽  
pp. 1365-1371 ◽  
Author(s):  
Jian Hong Xie
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Fuzzy Logic ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Damage ◽  
Least Square ◽  
Support Vector ◽  
Structural Damage Detection ◽  
Quantum Genetic Algorithm

Structural damage detection and health monitoring is very important in many applications, and a key related issue is the method of damage detection. In this paper, Fuzzy Least Square Support Vector Machine (FLS-SVM) is constructed by combining Fuzzy Logic with LS-SVM, and a real-coded Quantum Genetic Algorithm (QGA) is applied to optimize parameters of FLS-SVM. Then, the method of FLS-SVM integrated QGA is used to detect damages for fiber smart structures. The testing results show FLS-SVM possesses the higher detecting accuracy and the bitter dissemination ability than LS-SVM under the same conditions, and the parameters of FLS-SVM can be effectively optimized by the real-coded QGA. The proposed method of FLS-SVM integrated QGA is effective and efficient for structural damage detection.

scholarly journals Screw Remaining Life Prediction Based on Quantum Genetic Algorithm and Support Vector Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
Xiaochen Zhang ◽  
Dongxiang Jiang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Life Prediction ◽  
Ball Screw ◽  
Support Vector ◽  
Remaining Life ◽  
Quantum Genetic Algorithm ◽  
Training Samples ◽  
Life Prediction Model

To predict the remaining life of ball screw, a screw remaining life prediction method based on quantum genetic algorithm (QGA) and support vector machine (SVM) is proposed. A screw accelerated test bench is introduced. Accelerometers are installed to monitor the performance degradation of ball screw. Combined with wavelet packet decomposition and isometric mapping (Isomap), the sensitive feature vectors are obtained and stored in database. Meanwhile, the sensitive feature vectors are randomly chosen from the database and constitute training samples and testing samples. Then the optimal kernel function parameter and penalty factor of SVM are searched with the method of QGA. Finally, the training samples are used to train optimized SVM while testing samples are adopted to test the prediction accuracy of the trained SVM so the screw remaining life prediction model can be got. The experiment results show that the screw remaining life prediction model could effectively predict screw remaining life.

Mutation probability of cytochrome P450 based on a genetic algorithm and support vector machine

2011 ◽  
Vol 6 (11) ◽  
pp. 1367-1376 ◽  
Author(s):  
Yu Yao ◽  
Tao Zhang ◽  
Yi Xiong ◽  
Li Li ◽  
Juan Huo ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Cytochrome P450 ◽  
Support Vector ◽  
Mutation Probability
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