[1]许进超,杨翠丽,乔俊飞,等.基于自组织模糊神经网络溶解氧控制方法研究[J].智能系统学报,2018,13(06):905-912.[doi:10.11992/tis.201801019]
 XU Jinchao,YANG Cuili,QIAO Junfei,et al.Dissolved oxygen concentration control method based on self-organizing fuzzy neural network[J].CAAI Transactions on Intelligent Systems,2018,13(06):905-912.[doi:10.11992/tis.201801019]
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基于自组织模糊神经网络溶解氧控制方法研究(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年06期
页码:
905-912
栏目:
出版日期:
2018-10-25

文章信息/Info

Title:
Dissolved oxygen concentration control method based on self-organizing fuzzy neural network
作者:
许进超12 杨翠丽12 乔俊飞12 马士杰12
1. 北京工业大学 信息学部, 北京 100124;
2. 计算智能与智能系统北京市重点实验室, 北京 100124
Author(s):
XU Jinchao12 YANG Cuili12 QIAO Junfei12 MA Shijie12
1. Faculty of Information Technology, Beijng University of technology, Beijing 100124, China;
2. Beijing Key Laboratory of Computational Intelligence and Intelligence System, Beijing 100124, China
关键词:
污水处理溶解氧过程控制神经网络自组织
Keywords:
wastewater treatmentdissolved oxygenprocess controlneural networkself-organization
分类号:
TP183
DOI:
10.11992/tis.201801019
摘要:
针对污水处理过程中溶解氧浓度难以控制的问题,提出了一种基于自组织模糊神经网络(self-organizing fuzzy neural network, SOFNN)的溶解氧(dissolved oxygen, DO)控制方法。首先,采用激活强度和神经元重要性两个评判标准,来判断神经元对网络的贡献及活跃程度。然后,对不活跃的神经元进行删减,以此来对神经网络结构进行自适应的调整,从而满足实际控制要求,提高控制精度。其次,采用梯度下降算法对SOFNN神经网络的各个参数进行实时调整,以保证网络的精度。最后,将该自组织方法用在Mackey-Glass时间序列预测中,结果表明所提出的自组织模糊神经网络具有较好的预测效果;同时将所提出的SOFNN方法在BSM1仿真平台上进行实验验证。结果表明,所提出的自组织模糊神经网络控制方法能够对溶解氧浓度进行较好地控制,具有一定的自适应能力。
Abstract:
It is difficult to control the dissolved oxygen (DO) concentration in wastewater treatment processes. To solve this problem, this paper proposes a dissolved oxygen control method based on a self-organizing fuzzy neural network (SOFNN). First, two judging criteria, firing strength and neuron importance, were used to determine the contribution and activity of neurons to the network. Then the inactive neurons were deleted to adjust the structure of the neural network to adaptively meet the actual control requirements and improve control accuracy. Second, a gradient descent algorithm was used to update the SOFNN parameters to ensure accuracy of the neural network. Finally, the proposed algorithm was used for the Mackey-Glass time series prediction, and the results showed that the proposed SOFNN had better prediction performance. Furthermore, the proposed SOFNN method was used on the benchmark simulation model no.1 (BSM1). The results indicate that the proposed SOFNN controller can achieve a better control effect for the DO control and has a good adaptive ability.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-01-11。
基金项目:国家自然科学基金项目(61533002,61603012);北京市教育委员会科研计划项目(KM201710005025).
作者简介:许进超,女,1992年生,硕士研究生,主要研究方向为污水处理过程智能控制;杨翠丽,女,1986年生,讲师,博士研究生,主要研究方向为进化算法、智能信息处理。发表学术论文10余篇,其中SCI检索7篇,EI检索12篇;乔俊飞,男,1968年生,教授,博士生导师,国家杰出青年基金获得者,教育部新世纪优秀人才,北京市精品课程负责人,主要研究方向为智能信息处理、智能优化控制。近年发表学术论文近70篇,被SCI检索15篇。获教育部科技进步奖一等奖和北京市科学技术奖三等奖各1项,获得授权国家发明专利12项。
通讯作者:许进超.E-mail:winadream@163.com
更新日期/Last Update: 2018-12-25