China's coal consumption forecasting using adaptive differential evolution algorithm and support vector machine

2021 ◽  
Vol 74 ◽  
pp. 102287
Author(s):  
Shi Mengshu ◽  
Huang Yuansheng ◽  
Xu Xiaofeng ◽  
Liu Dunnan
Keyword(s):  
Support Vector Machine ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Support Vector ◽  
Coal Consumption ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm

scholarly journals Artificial Intelligence Education System Based on Differential Evolution Algorithm to Optimize SVM

2022 ◽  
Vol 2022 ◽  
pp. 1-7
Author(s):  
Weilin Long ◽  
Yi Gao
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machine ◽  
Differential Evolution ◽  
Education System ◽  
Classification Accuracy ◽  
Differential Evolution Algorithm ◽  
Support Vector ◽  
Machine Model ◽  
Evolution Algorithm ◽  
Intelligence Education

The artificial intelligence education system promotes the rooting of artificial intelligence in the education field and accelerates its entry into the era of intelligent education. This article focuses on the development of the artificial intelligence education system and proposes an artificial intelligence education system based on differential evolution algorithm optimization support vector machine. First, the processing of educational demand information data is automated, then a differential evolution algorithm is built to optimize the support vector machine model, and the model is used to implement various educational tasks to achieve automated education. The test results show that the model classification accuracy, classification recall rate, classification accuracy rate, and F1-score value are 4 items. Performances have been improved to improve the efficiency of education work and provide a reference for exploring the application and practice of artificial intelligence in education.

Fault Diagnosis of Power Transformer Based on Adaptive Differential Evolution and Least Square Support Vector Machine

2013 ◽  
Vol 791-793 ◽  
pp. 912-916 ◽  
Author(s):  
Zi Pin Li ◽  
Hui Peng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Differential Evolution ◽  
Power Transformer ◽  
Differential Evolution Algorithm ◽  
Classification Problem ◽  
Small Sample ◽  
Least Square ◽  
Support Vector ◽  
Adaptive Differential Evolution

Least square support vector machine (LS-SVM) can solve small sample, high-dimensional and non-linear multi-classification problem well, so it is applicable to the power transformer fault diagnosis. However, the parameters of LS-SVM have significant effect on the classification results.In this paper, the adaptive differential evolution algorithm (ADE) is applied to optimize the parameters of LS-SVM. The scaling factor and crossover rate are adjusted dynamically in the whole evolution process, so the robustness of the algorithm is improved greatly. The optimized LS-SVM is applied to fault diagnosis of power transformer, the results obtained demonstrate superiority of the proposed approach.

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