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基于ELM-AdaBoost.M2的污水处理过程在线故障诊断
Alternative TitleOnline Fault Diagnosis of Wastewater Treatment Process Based on ELM-AdaBoost.M2
谭承诚; 于广平; 邱志成
Department广州中国科学院沈阳自动化研究所分所
Source Publication计算机测量与控制
ISSN1671-4598
2018
Volume26Issue:2Pages:53-56
Contribution Rank2
Funding Organization广东省科技项目(2016B090918113)
Keyword污水处理 故障诊断 极限学习机 Adaboost.m2 在线建模
Abstract污水处理存在着强非线性和非稳态运行等特征,对其运行过程进行在线故障诊断在减少污染和保障生产过程安全方面具有重大意义;针对污水处理过程运行状态的不平衡分布造成故障诊断准确率下降的问题,提出一种基于极限学习机(ELM)和AdaBoost.M2算法的在线故障诊断方法;该模型以ELM为弱分类器,利用AdaBoost.M2将多个弱分类器集成,实现了强分类器;仿真结果表明,该模型在线故障诊断精度高,学习速度快,泛化性能好,相较于传统故障诊断方法,综合性能较为突出,较好地实现了污水处理的在线故障诊断。
Other AbstractWastewater treatment exists strong nonlinearity, unsteady operation and other characteristics, the online fault diagnosis of wastewater treatment process in reducing pollution and ensure the safety is of great significance. Concerning the low accuracy of fault diagnosis induced by the unbalanced distribution of wastewater treatment process’running state, an online fault diagnosis model based on extreme learning machine (ELM)and AdaBoost.M2 is proposed. Firstly, set ELM as weak classifier and then use AdaBoost.M2 to embody several weak classifiers into strong classifier. The simulation experiments demonstrated that this online diagnosis model has higher precision, faster speed, better generalization ability, and outstanding performance, while comparing to the traditional ones. Therefore, the proposed model can meet the requirements of online fault diagnosis of wastewater treatment process 
Language中文
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/21571
Collection广州中国科学院沈阳自动化研究所分所
Corresponding Author谭承诚
Affiliation1.华南理工大学机械与汽车工程学院
2.中国科学院沈阳自动化研究所广州分所
Recommended Citation
GB/T 7714
谭承诚,于广平,邱志成. 基于ELM-AdaBoost.M2的污水处理过程在线故障诊断[J]. 计算机测量与控制,2018,26(2):53-56.
APA 谭承诚,于广平,&邱志成.(2018).基于ELM-AdaBoost.M2的污水处理过程在线故障诊断.计算机测量与控制,26(2),53-56.
MLA 谭承诚,et al."基于ELM-AdaBoost.M2的污水处理过程在线故障诊断".计算机测量与控制 26.2(2018):53-56.
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