Research on reliability analysis of catenary model based on the fusion particle swarm least square support vector machine algorithm

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Bulai Wang ◽  
Jinbai Zou ◽  
Decheng Zhao ◽  
Xuan Chen ◽  
Piao Liu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Reliability Analysis ◽  
Particle Swarm ◽  
Least Square ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Model Based

Particle swarm optimisation aided least-square support vector machine for load forecast with spikes

2016 ◽  
Vol 10 (5) ◽  
pp. 1145-1153 ◽  
Author(s):  
Whei-Min Lin ◽  
Ren-Fu Yang ◽  
Ming-Tang Tsai ◽  
Chia-Sheng Tu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Least Square ◽  
Support Vector ◽  
Load Forecast

Research on Particle Swarm Optimization Least Square Support Vector Machine for Short-Term Wind Speed Prediction

2014 ◽  
Vol 511-512 ◽  
pp. 927-930
Author(s):  
Shuai Zhang ◽  
Hai Rui Wang ◽  
Jin Huang ◽  
He Liu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Wind Speed ◽  
Particle Swarm ◽  
Least Square ◽  
Support Vector ◽  
Short Term ◽  
Swarm Optimization ◽  
Wind Speed Prediction ◽  
Speed Prediction

In the paper, the forecast problems of wind speed are considered. In order to enhance the redaction accuracy of the wind speed, this article is about a research on particle swarm optimization least square support vector machine for short-term wind speed prediction (PSO-LS-SVM). Firstly, the prediction models are built by using least square support vector machine based on particle swarm optimization, this model is used to predict the wind speed next 48 hours. In order to further improve the prediction accuracy, on this basis, introduction of the offset optimization method. Finally large amount of experiments and measurement data comparison compensation verify the effectiveness and feasibility of the research on particle swarm optimization least square support vector machine for short-term wind speed prediction, Thereby reducing the short-term wind speed prediction error, very broad application prospects.

Modeling of Flexible Structures by Means of Least Square Support Vector Machine

2015 ◽  
Author(s):  
Hammam Tamimi ◽  
Dirk Söffker
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Flexible Structures ◽  
Operating Conditions ◽  
Least Square ◽  
Support Vector ◽  
Suitable Model ◽  
Support Vector Machine Algorithm ◽  
Training Time ◽  
Svm Algorithm

This paper investigates modeling of flexible structures by means of the least squares support vector machine (LS-SVM) algorithm. Modeling is the first step to obtain a suitable model-based controller for any given system. Accurate modeling of a flexible structure based on experimental data using LS-SVM algorithm requires less knowledge about the physical system. Least squares support vector machine algorithm can achieve global and unique solution when compared with other soft computing algorithms. Also, LS-SVM algorithm requires less training time. In this paper, the successful use of support vector machine algorithm to model the flexible cantilever is demonstrated. The acquired model is able to provide accurate prediction of the system output under different operating conditions. Experimental results demonstrate the efficiency and high precision of the proposed approach.

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