Parsimonious Support Vector Machines Modelling for Set Points in Industrial Processes Based on Genetic Algorithm Optimization

2014 ◽  
pp. 1-10 ◽  
Author(s):  
Andrés Sanz-García ◽  
Julio Fernández-Ceniceros ◽  
Fernando Antoñanzas-Torres ◽  
F. Javier Martínez-de-Pisón-Ascacibar
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machines ◽  
Industrial Processes ◽  
Support Vector ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Vector Machines

scholarly journals Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Qiang Liu ◽  
Songyong Liu ◽  
Qianjin Dai ◽  
Xiao Yu ◽  
Daoxiang Teng ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Back Propagation ◽  
Operating Conditions ◽  
Support Vector ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Fault Detection And Identification ◽  
Vector Machines

Incipient fault detection and identification (IFDI) of cutting arms is a crucial guarantee for the smooth operation of a roadheader. However, the shortage of fault samples restricts the application of the fault diagnosis technique, and the data analysis tools should be optimized efficiently. In this study, four machine learning tools (the back-propagation neural network based on genetic algorithm optimization, the naive Bayes based on genetic algorithm optimization, the support vector machines based on particle swarm optimization, and the support vector machines based on dynamic cuckoo) are applied to address the challenge in the IFDI of cutting arms. The commonly measured current and vibration data cutting arms are used in the IFDI. The experimental results show that the support vector machines based on dynamic cuckoo outperform the other methods. Besides, the performance of the four methods under different operating conditions is compared. The fault cause of cutting arms of the roadheader is analyzed and the design improvement scheme for cutting arms is provided. This study provides a reference for improving the fault diagnosis of the roadheader.

scholarly journals Identifying Travel Mode with GPS Data Using Support Vector Machines and Genetic Algorithm

Information ◽  
2015 ◽  
Vol 6 (2) ◽  
pp. 212-227 ◽  
Author(s):  
Fang Zong ◽  
Yu Bai ◽  
Xiao Wang ◽  
Yixin Yuan ◽  
Yanan He
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machines ◽  
Support Vector ◽  
Gps Data ◽  
Travel Mode ◽  
Vector Machines

Patent Innovation Factors Evolution Based on P-SVM and GCCA

2010 ◽  
Vol 39 ◽  
pp. 247-252
Author(s):  
Sheng Xu ◽  
Zhi Juan Wang ◽  
Hui Fang Zhao
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machines ◽  
Correlation Coefficient ◽  
Network Architecture ◽  
Innovation Management ◽  
Model Development ◽  
Real Data ◽  
Support Vector ◽  
Vector Machines ◽  
Gray Correlation

A two-stage neural network architecture constructed by combining potential support vector machines (P-SVM) with genetic algorithm (GA) and gray correlation coefficient analysis (GCCA) is proposed for patent innovation factors evolution. The enterprises patent innovation is complex to conduct due to its nonlinearity of influenced factors. It is necessary to make a trade off among these factors when some of them conflict firstly. A novel way about nonlinear regression model with the potential support vector machines (P-SVM) is presented in this paper. In the model development, the genetic algorithm is employed to optimize P-SVM parameters selection. After the selected key factors by the PSVM with GA model, the main factors that affect patent innovation generation have been quantitatively studied using the method of gray correlation coefficient analysis. Using a set of real data in China, the results show that the methods developed in this paper can provide valuable information for patent innovation management and related municipal planning projects.

Genetic algorithm based on support vector machines for computer vision syndrome classification in health personnel

2018 ◽  
Vol 32 (5) ◽  
pp. 1239-1248 ◽  
Author(s):  
Eva María Artime Ríos ◽  
Ana Suárez Sánchez ◽  
Fernando Sánchez Lasheras ◽  
María del Mar Seguí Crespo
Keyword(s):  
Genetic Algorithm ◽  
Computer Vision ◽  
Support Vector Machines ◽  
Health Personnel ◽  
Support Vector ◽  
Computer Vision Syndrome ◽  
Vector Machines ◽  
Syndrome Classification
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