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.

Particle Swarm Optimisation-Support Vector Machine Optimised by Association Rules for Detecting Factors Inducing Heart Diseases

2017 ◽  
Vol 26 (3) ◽  
pp. 573-583
Author(s):  
Lu Wei-Jia ◽  
Ma Liang ◽  
Chen Hao
Keyword(s):  
Support Vector Machine ◽  
Association Rules ◽  
Heart Diseases ◽  
Particle Swarm ◽  
Detection Algorithm ◽  
Classification Performance ◽  
Particle Swarm Optimisation ◽  
Support Vector ◽  
Classification Algorithms ◽  
Data Set

AbstractExisting systems for diagnosing heart diseases are time consuming, expensive, and error prone. Aiming at this, a detection algorithm for factors inducing heart diseases based on a particle swarm optimisation-support vector machine (PSO-SVM) optimised by association rules (ARs) was proposed. Firstly, AR was used to select features from a disease data set so as to train feature sets. Then, PSO-SVM was used to classify training and testing sets, and then the factors inducing heart diseases were analysed. Finally, the effectiveness and reliability of the proposed algorithm was verified by experiments on the UCI Cleveland data set with confidence as the index. The experimental results showed that females have less risk of having a heart attack than males. Irrespective of gender, once diagnosed with chest pain without symptoms and angina caused by exercise, people are more likely to suffer from heart disease. Moreover, compared with another two advanced classification algorithms, the proposed algorithm showed better classification performance and therefore can be used as a powerful tool to help doctors diagnose and treat heart diseases.

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