CAJ | 학술논문

提出智能优化支持向量机算法来提高模型的预测能力和泛化能力.该算法针对支持向量机噪声敏感问题采用小波方法对数据集去噪;利用核主成分分析方法提取数据特征;采用量子粒子群算法优化支持向量机超参数.将该优化算法应用于锅炉负荷短期预测,实验结果表明,该优化算法预测精度较高,收敛速度较快,泛化性能优于其他预测方法,且工程实现容易.
제출지능우화지지향량궤산법래제고모형적예측능력화범화능력.해산법침대지지향량궤조성민감문제채용소파방법대수거집거조;이용핵주성분분석방법제취수거특정;채용양자입자군산법우화지지향량궤초삼수.장해우화산법응용우과로부하단기예측,실험결과표명,해우화산법예측정도교고,수렴속도교쾌,범화성능우우기타예측방법,차공정실현용역.
Intelligently optimal support vector machine (SVM) were introduced in electric utility boiler to improve short-term load forecasting accuracy and generalization ability. Wavelet transform is adopted to filter noise in training and testing data set. Kernel principle component analysis is used in feature selection. Then quantum-behaved particle swarm algorithm is chosen to determinate optimal hyper-parameter in SVM. This optimal algorithm has been tested on power plant and the results show that the prediction can get higher precision and convergence speed.