Comprehensive Evaluation of Cleaner Production in Thermal Power Plants Using Particle Swarm Optimization Based Least Squares Support Vector Machines

2015 ◽  
Vol 12 (5) ◽  
pp. 1993-2000 ◽  
Author(s):  
Wei Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Least Squares ◽  
Power Plants ◽  
Comprehensive Evaluation ◽  
Thermal Power ◽  
Support Vector ◽  
Thermal Power Plants ◽  
Swarm Optimization ◽  
Vector Machines

Fuel Identification Based on the Least Squares Support Vector Machines

2011 ◽  
Vol 317-319 ◽  
pp. 1237-1240 ◽  
Author(s):  
Yao Song Huang ◽  
Shi Liu ◽  
Jie Li ◽  
Lei Jia ◽  
Zhi Hong Li
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Power Plants ◽  
Detection System ◽  
Support Vector ◽  
Correct Identification ◽  
Identification Rate ◽  
Vector Machines ◽  
Flame Detection ◽  
Correct Identification Rate

The identification of the fuel types plays an important role in ensuring the safety and economics of the power plants. In order to obtain the flame signal in the process of combustion, a flame detection system is designed and a laboratorial platform is constructed. This paper extracts the signal parameters—the mean, the peak-peak value, the flicker frequency, and the flicker intensity —and takes them as the characteristic quantities of the flame signal. Based on the least squares support vector machines (LSSVM), an efficient method of identifying the flame types is developed. The result of the identification is more ideal, with the correct identification rate up to 100%. This shows that the method combined the four characteristic quantities with the LSSVM can obtain a good result in the identification of the fuel types.

Parameter Identification of Ship Maneuvering Model Based on Support Vector Machines and Particle Swarm Optimization

2016 ◽  
Vol 138 (3) ◽  
Author(s):  
Weilin Luo ◽  
C. Guedes Soares ◽  
Zaojian Zou
Keyword(s):  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Difference Method ◽  
Particle Swarm ◽  
Support Vector ◽  
Ship Maneuvering ◽  
Swarm Optimization ◽  
Hydrodynamic Derivatives ◽  
Vector Machines ◽  
Maneuvering Motion

Combined with the free-running model tests of KVLCC ship, the system identification (SI) based on support vector machines (SVM) is proposed for the prediction of ship maneuvering motion. The hydrodynamic derivatives in an Abkowitz model are determined by the Lagrangian factors and the support vectors in the SVM regression model. To obtain the optimized structural factors in SVM, particle swarm optimization (PSO) is incorporated into SVM. To diminish the drift of hydrodynamic derivatives after regression, a difference method is adopted to reconstruct the training samples before identification. The validity of the difference method is verified by correlation analysis. Based on the Abkowitz mathematical model, the simulation of ship maneuvering motion is conducted. Comparison between the predicted results and the test results demonstrates the validity of the proposed methods in this paper.

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