| 引用本文: | 丛秋梅,柴天佑,余 文.污水处理过程的递阶神经网络建模[J].控制理论与应用,2009,26(1):8~14.[点击复制] |
| CONG Qiu-mei,CHAI Tian-you,YU Wen.Modeling wastewater treatment plant via hierarchical neural networks[J].Control Theory and Technology,2009,26(1):8~14.[点击复制] |
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| 污水处理过程的递阶神经网络建模 |
| Modeling wastewater treatment plant via hierarchical neural networks |
| 摘要点击 3084 全文点击 2205 投稿时间：2007-12-29 修订日期：2008-07-11 |
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| DOI编号 10.7641/j.issn.1000-8152.2009.1.002 |
| 2009,26(1):8-14 |
| 中文关键词 污水处理过程 串级过程 递阶神经网络 稳定学习律 |
| 英文关键词 wastewater treatment plant cascaded process hierarchical neural networks stable learning law |
| 基金项目 国家重点基础研究发展(973)计划资助项目(2002CB312201); 国家自然科学基金重点资助项目(60534010); 国家创新研究群体科学基金资助项目(60521003); 长江学者和创新团队发展计划资助资助(IRT0421); 111工程资助项目(B08015). |
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| 中文摘要 |
| 针对污水处理过程的多变量和多非线性子系统的串级结构特点, 提出了一种基于活性污泥过程机理的递 阶神经网络建模方法.该方法将神经网络与过程机理模型以串级方式连接, 以神经网络辨识活性污泥过程模型中的非线性组分反应速率. 分析各子过程建模误差的关系, 给出了模型的稳定学习算法和稳定性理论分析. 最后通过某污水处理厂生化脱氮过程实际运行数据的实验表明所提出的建模方法是有效的. |
| 英文摘要 |
| A hierarchical neural networks based on the mechanism of activated sludge process is introduced for modeling the wastewater treatment plant (WWTP) which includes multivariable and multi-nonlinear subsystems with serial structure. This approach combines the neural network and the mechanism model in a serial configuration; and the nonlinear uncertainties
of the activated sludge process are estimated by neural networks. A stable learning algorithm and the theoretical analysis are given for this model based on the relations of various modeling errors among sub-processes. Operational data of a wastewater treatment plant illustrate the efficacy of this modeling approach. |