CAJ | 학술논문

针对高炉炼铁智能控制专家系统中单一支持向量机（SVM）炉温预测模型的改进研究,提出一种基于模糊C均值聚类（FCM）的多支持向量机模型。首先运用模糊C均值聚类对模型训练集进行聚类划分,然后对每一类进行支持向量机的训练,建立相应的子模型,并对测试集中的同一样本点分别进行预测,以测试样本点的输入对应于每一类的隶属度为权值,进行加权求和,最终得到预测值。通过对在线采集的数据分析表明,基于FCM的多支持向量机模型比单一的支持向量机模型在多方面预测性能得到改善,连续预测100炉命中率达86%。
침대고로련철지능공제전가계통중단일지지향량궤（SVM）로온예측모형적개진연구,제출일충기우모호C균치취류（FCM）적다지지향량궤모형。수선운용모호C균치취류대모형훈련집진행취류화분,연후대매일류진행지지향량궤적훈련,건립상응적자모형,병대측시집중적동일양본점분별진행예측,이측시양본점적수입대응우매일류적대속도위권치,진행가권구화,최종득도예측치。통과대재선채집적수거분석표명,기우FCM적다지지향량궤모형비단일적지지향량궤모형재다방면예측성능득도개선,련속예측100로명중솔체86%。
A kind of multiple support vector machines model based on fuzzy C-mean clustering （FCM） is proposed in order to improve the temperature prediction model with single support vector machine （SVM） in intelligent control expert system for blast furnace. At first, the training set is clas- sified into several groups by using of fuzzy C-mean clustering method. Then each group is trained through support vector machine and the corresponding sub-models are established. After that, the same sample in testing set is predicted separately and the memberships between each sample and each group are set as the weights. Finally, the predicted result is obtained by weighted sum. The analysis of on-line data shows that the multiple support vector machines model based on fuzzy C-mean clustering has. better prediction performance in many aspects compared with the single SVM model. The hit rate reaches 86% in consecutive 100 furnaces based on the proposed model.