Study of fault diagnosis method for three-phase high power factor rectifier based on PSO-LSSVM algorithm

Zhang, Shutuan; Zhang, Kai; Jiang, Jing

doi:10.1109/asemd.2009.5306653

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…In the spirit of GA-LSSVM, at present, many researchers have used PSO or its improved algorithms to optimise LSSVM. In order to enhance fault diagnosis precision for electric equipment, the LSSVM algorithm based on PSO was proposed in Zhang et al (2009). The experiments showed that the PSO-LSSVM algorithm had better fault diagnosis ability than LSSVM.…”

Section: Research Progress On Parameters Optimisation Of Svm With Psomentioning

confidence: 99%

Research on parameters optimisation of SVM based on swarm intelligence

Ding¹,

Huang²

2014

IJCI

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Support vector machine (SVM) is a new machine learning method based on statistical learning theory, which has become a hot research topic in the field of machine learning because of its excellent performance. However, the performance of SVM is very sensitive to its parameters. At present, swarm intelligence is the most common method to optimise the parameters of SVM. In this paper, the research on parameters optimisation of SVM based on swarm intelligence algorithms is reviewed. Firstly, we briefly introduce the theoretical basis of SVM. Secondly, we describe the latest progress of parameters optimisation of SVM based on swarm intelligence in recent years. Finally, we point out the research and development prospects of this kind of method.

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Section: Research Progress On Parameters Optimisation Of Svm With Psomentioning

confidence: 99%

Research on parameters optimisation of SVM based on swarm intelligence

Ding¹,

Huang²

2014

IJCI

View full text Add to dashboard Cite

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Interbank Offered Rate Forecasting Using PSO-LS-SVM

Lin

Tang

2015

2015 11th International Conference on Computational Intelligence and Security (CIS)

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In the procedure of China's market-oriented reforms of interest rates, the interest-rate risk becomes increasingly apparent. Analyzing the existing studies and the interbank offered rate trend, this paper finds that LS-SVM, which is short for Least Squares Support Vector Machines, is excel in nonlinear data approximation, which is suitable for interest rate forecasting. Firstly, the paper established a standard LS-SVM model. Secondly, Particle Swarm Optimization is introduced to optimize the parameters of LS-SVM. Thirdly, a standard SVM model optimized by PSO and a BP neural network are established for comparison. Then the experiment is conduct to forecast the offered rate in China's interbank market.The results show that the PSO-LS-SVM model outperforms any other approaches since its RMSE of testing is 0.04 and the relative errors between forecasting values and the actual ones are all below 0.18, which demonstrates the performance of the PSO-LS-SVM model in interest rate forecasting is promising.

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Study of fault diagnosis method for three-phase high power factor rectifier based on PSO-LSSVM algorithm

Cited by 2 publications

References 8 publications

Research on parameters optimisation of SVM based on swarm intelligence

Research on parameters optimisation of SVM based on swarm intelligence

Interbank Offered Rate Forecasting Using PSO-LS-SVM

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