电气工程学报 ›› 2017, Vol. 12 ›› Issue (11): 21-27.doi: 10.11985/2017.11.004

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基于RS和LS-SVM的回转窑主传动电流预测研究

何孟凡,艾红   

  1. 北京信息科技大学自动化学院 北京 100192
  • 收稿日期:2017-06-19 出版日期:2017-11-25 发布日期:2017-11-25
  • 作者简介:何孟凡 男 1991年生,硕士研究生,主要研究方向为检测技术与自动化装置。|艾 红 女 1962年生,教授,硕士生导师,主要研究方向为故障检测与诊断。
  • 基金资助:
    北京市自然科学基金资助项目(4162025)

Prediction of Rotary Kiln Based on LS-SVM andRS Drive Current

He Mengfan,Ai Hong   

  1. Beijing Information Science & Technology University Beijing 100192 China
  • Received:2017-06-19 Online:2017-11-25 Published:2017-11-25

摘要:

水泥回转窑熟料制作过程中主传动电机电流不稳定、波动范围大,文章结合粗糙集、最小二乘支持向量机原理对水泥回转窑主传动电流进行预测。首先介绍粗糙集、最小二乘支持向量机的原理,通过搜集影响水泥回转窑主传动电流变化的数据建立信息决策表并对其进行预处理,使用粗糙集对样本数据进行约简,包括属性约简、属性值约简,利用 LS-SVM理论对约简后的数据进行处理及预测,并将其他数据用于训练测试,验证测试结果。融合后的方法克服了LS-SVM对冗余信息和关键信息识别的局限性,补偿RS理论对输入数据信息缺乏抗干扰能力的缺点,通过实验研究证明该方法有较强的泛化能力,且预测准确率高。

关键词: 最小二乘支持向量机, 粗糙集, 水泥回转窑, 主传动电流

Abstract:

Based on rough set, the least squares support vector machine (SVM) were used to predict the main drive of cement rotary kiln current, aiming at the unsteady and changing characteristics of cement kiln clinker production process. Firstly, the concept of rough set and least squares support vector machine is introduced, the current data is collected through database to establish the information table and preprocess it,the rough data is used to reduce the original data, the LS-SVM theory is used to classify and reduce the data. This fusion method overcomes the shortcoming of LS-SVM's recognition of redundant information and key information, and makes up the shortcomings of RS theory on the anti-jamming ability of input information. It is proved that this method has strong generalization ability and accurate prediction High rate.

Key words: Least square support vector machine, rough set, rotary cement kiln, main drive current

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