Automatic configuration of optimized sample-weighted least-squares support vector machine by particle swarm optimization for multivariate spectral analysis

2010 ◽  
Vol 2 (3) ◽  
pp. 282 ◽  
Author(s):  
Hai-Yan Fu ◽  
Hai-Long Wu ◽  
Hong-Yan Zou ◽  
Li-Juan Tang ◽  
Lu Xu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Spectral Analysis ◽  
Particle Swarm Optimization ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Particle Swarm ◽  
Support Vector ◽  
Swarm Optimization

Power Transformer Fault Diagnosis Based on Least Squares Support Vector Machine and Particle Swarm Optimization

2011 ◽  
Vol 50-51 ◽  
pp. 624-628
Author(s):  
Xin Ma
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Power Transformer ◽  
Particle Swarm ◽  
Support Vector ◽  
Risk Minimization ◽  
Swarm Optimization ◽  
Transformer Fault

Dissolved gas analysis (DGA) is an important method to diagnose the fault of power t ransformer. Least squares support vector machine (LS-SVM) has excellent learning, classification ability and generalization ability, which use structural risk minimization instead of traditional empirical risk minimization based on large sample. LS-SVM is widely used in pattern recognition and function fitting. Kernel parameter selection is very important and decides the precision of power transformer fault diagnosis. In order to enhance fault diagnosis precision, a new fault diagnosis method is proposed by combining particle swarm optimization (PSO) and LS-SVM algorithm. It is presented to choose σ parameter of kernel function on dynamic, which enhances precision rate of fault diagnosis and efficiency. The experiments show that the algorithm can efficiently find the suitable kernel parameters which result in good classification purpose.

scholarly journals Cost prediction on fabricated substation considering support vector machine via optimized quantum particle swarm optimization

2020 ◽  
Vol 24 (5 Part A) ◽  
pp. 2773-2780
Author(s):  
Yi Xiong ◽  
Yue Ming ◽  
Xiaohong Liao ◽  
Chuanyu Xiong ◽  
Wu Wen ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Life Cycle ◽  
Least Squares ◽  
Life Cycle Cost ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Support Vector ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

At present, the prediction of the life cycle cost of fabricated substation is of great significance for the construction of fabricated substation. An enhanced prediction model based on quantum particle swarm optimization (QPSO) via least squares support vector machine is established. The relevant characteristic index of the life cycle of the fabricated substation is used as the input of the model, and the output is the life cycle cost. The simulation results are compared with the prediction results of QPSO optimized least squares support vector machine (LS-SVM), PSO optimized LS-SVM, traditional LS-SVM, and backpropagation neural network, which shows that the QPSO optimized LS-SVM model has better prediction accuracy, can predict and evaluate the life cycle cost more quickly, and can improve the benefits of fabricated substation construction.

scholarly journals Water demand forecast model of Least Squares Support Vector Machine based on Particle Swarm Optimization

2018 ◽  
Vol 246 ◽  
pp. 01029 ◽  
Author(s):  
Kun Yan ◽  
Min-Zhi Yang
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Least Squares ◽  
Water Demand ◽  
Particle Swarm ◽  
Forecast Model ◽  
Support Vector ◽  
Demand Forecast ◽  
Swarm Optimization ◽  
Water Demand Forecast

In order to solve the problem of precision of water demand forecast model, a coupled water demand forecast model of particle swarm optimization (PSO) algorithm and least squares support vector machine (LS-SVM) are proposed in this paper. A PSO-LSSVM model based on parameter optimization was constructed in a coastal area of Binhai, Jiangsu Province, and the total water demand in 2009 and 2010 were simulated and forecasted with the absolute value of the relative errors less than 2.1%. The results showed that the model had good simulation effect and strong generalization performance, and can be widely used to solve the problem of small- sample, nonlinear and high dimensional water demand forecast.

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