Statistically validated QSAR study of some antagonists of the human CCR5 receptor using least square support vector machine based on the genetic algorithm and factor analysis

A machine vision system was developed to investigate the detection of watermelon seeds exterior quality. The main characteristics of watermelon seeds appearance including area, perimeter, roughness and minimum enclosing rectangle were calculated by image analysis. Least square support vector machine optimized by genetic algorithm was applied for the classification of watermelon seeds exterior quality, and the broken seeds, normal seeds and high-quality seeds were distinguished finally. The surface irregularities defects of watermelon seeds were detected by machine vision grid laser. The experimental results show that the watermelon seeds exterior quality could be well detected and classified by machine vision based on least squares support vector machine.

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Contamination degree prediction of insulator surface based on exploratory factor analysis‐least square support vector machine combined model

High Voltage ◽

10.1049/hve2.12019 ◽

2020 ◽

Author(s):

Jiaxiang Sun ◽

Hongru Zhang ◽

Qingquan Li ◽

Hongshun Liu ◽

Xinbo Lu ◽

...

Keyword(s):

Support Vector Machine ◽

Factor Analysis ◽

Exploratory Factor Analysis ◽

Least Square ◽

Support Vector ◽

Combined Model ◽

Insulator Surface ◽

Contamination Degree

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A prediction method for deck-motion based on online least square support vector machine and genetic algorithm

Journal of Marine Science and Technology ◽

10.1007/s00773-018-0557-z ◽

2018 ◽

Vol 24 (2) ◽

pp. 382-397

Author(s):

Xixiang Liu ◽

Qiming Wang ◽

Rong Huang ◽

Songbing Wang ◽

Xianjun Liu

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

Prediction Method ◽

Least Square ◽

Support Vector

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Fault Diagnosis of Wind Turbine Gearbox Based on Least Square Support Vector Machine with Genetic Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.846-847.620 ◽

2013 ◽

Vol 846-847 ◽

pp. 620-623

Author(s):

Wen Qing Zhao ◽

Rui Cai ◽

Li Wei Wang ◽

De Wen Wang

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

Fault Diagnosis ◽

Wind Turbine ◽

Least Square ◽

Normal Operation ◽

Support Vector ◽

Wind Turbine Gearbox ◽

Vector Method ◽

Searching Ability

Gearbox affect the normal operation of the wind turbines, to study the fault diagnosis, support vector method was used. Parameters selection is very important and decides the fault diagnosis precision. In order to overcome the blindness of man-made choice of the parameters in least squares support vector machine (LSSVM) and improve the accuracy and efficiency of fault diagnosis, a method based on LSSVM trained by genetic algorithm was proposed. This method searches the optimized parameters in LSSVM by taking advantage of the genetic algorithms powerful global searching ability. The research is provided using this method on the fault diagnosis of wind turbine gearbox and compared with the diagnostic method of LSSVM. The experimental results show that the method achieves a higher diagnostic accuracy.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.20-23.1365 ◽

2010 ◽

Vol 20-23 ◽

pp. 1365-1371 ◽

Cited By ~ 1

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.

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