基于元学习的污水水质集成软测量模型 | |
庞强![]() ![]() ![]() | |
刊名 | 信息与控制
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2014 | |
卷号 | 43期号:2页码:248-252 |
关键词 | 污水处理 软测量 自适应加权融合 元学习 |
ISSN号 | 1002-0411 |
其他题名 | Soft-Sensor of Water Quality Based on Integrated ELM with Meta-Learning |
产权排序 | 1 |
中文摘要 | 针对污水处理过程在运行工况频繁波动的情况下,单一水质软测量模型精度下降的问题,提出了污水水质集成软测量建模方法.模型由3层结构组成:基于模糊聚类-极限学习机(ELM,extreme learning machine)的预测子模型位于最底层,第2层采用自适应加权融合方法将子模型预测值进行集成,最上层采用基于信息熵的元学习机制管理融合权值.ELM的快速学习特点使模型具有较好的实时性能,自适应加权融合方法和元学习机制提高了模型泛化性,元学习机制跟踪污水处理过程运行状况的动态变化趋势.仿真结果表明,在多工况条件下,污水水质COD(chemical oxygen demand,化学需氧量)集成软测量模型... |
英文摘要 | A soft-sensor of water quality for wastewater treatment plants,which is based on an integrated model,is presented. The proposed soft-sensor aims to address the difficulty in using a single model to represent the characteristics of wastewater treatment processes with varying operating regimes. The soft-sensor is composed of three layers,in which a predictive sub-model based on FCM-ELMs are the bottom layer,adaptive weighted fusion method fusing predictive values of the sub-model are the middle layer,and a meta-learning mechanism based on information entropy updating fusion weights is the top layer. The meta-learning mechanism can track the dynamic trend of operating conditions of wastewater treatment plants. The quick learning advantage of ELM results in the soft-sensor showing excellent real-time performance. The adaptive weighted fusion method and meta-learning mechanism improve the model generalization. Simulation results show that the integrated model for COD is more accurate than other models. |
收录类别 | CSCD |
资助信息 | 中国博士后科学基金面上资助项目(2013M530953,2013M532118); 国家自然科学基金资助项目(61034008,61004051) |
语种 | 中文 |
CSCD记录号 | CSCD:5139190 |
公开日期 | 2014-11-03 |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.ac.cn/handle/173321/15192] ![]() |
专题 | 沈阳自动化研究所_信息服务与智能控制技术研究室 |
推荐引用方式 GB/T 7714 | 庞强,苑明哲,王景杨. 基于元学习的污水水质集成软测量模型[J]. 信息与控制,2014,43(2):248-252. |
APA | 庞强,苑明哲,&王景杨.(2014).基于元学习的污水水质集成软测量模型.信息与控制,43(2),248-252. |
MLA | 庞强,et al."基于元学习的污水水质集成软测量模型".信息与控制 43.2(2014):248-252. |
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