| 基于SOTSFNN的溶解氧浓度控制方法 |
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| 引用本文： | 乔俊飞,付文韬,韩红桂. 基于SOTSFNN的溶解氧浓度控制方法[J]. 化工学报, 2016, 67(3): 960-966. DOI: 10.11949/j.issn.0438-1157.20151924 |
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| 作者姓名： | 乔俊飞 付文韬 韩红桂 |
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| 作者单位： | 北京工业大学电子信息与控制工程学院, 计算智能与智能系统北京市重点实验室, 北京 100124 |
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| 基金项目： | 国家自然科学基金项目(61533002,61203099,61225016);北京市科技新星计划项目(Z131104000413007);教育部博士点新教师基金项目(20121103120020);北京市教育委员会科研计划项目(KZ201410005002,km201410005001);北京市朝阳区协同创新项目(ZH14000177);高等学校博士学科点专项科研基金资助课题项目(20131103110016)。 |
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| 摘 要： | 针对污水处理过程溶解氧浓度难以控制的问题,提出了一种基于自组织T-S模糊神经网络的控制方法。其实质是采用模糊规则层激活强度的方法,根据实际环境自适应的对神经元进行调整,构造合适的控制结构,从而提高控制精度。同时采用梯度下降法对控制器的各个参数进行实时调整。该控制器运用在污水处理基准仿真模型中进行实验,结果表明,提出的SO-TSFNN控制方法能够较好地实现对溶解氧浓度的控制,具有较好的自适应性。
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| 关 键 词： | 神经网络 控制 溶解氧浓度 动态仿真 自组织算法 |
| 收稿时间： | 2015-12-18 |
| 修稿时间： | 2015-12-28 |
Dissolved oxygen control method based on self-organizing T-S fuzzy neural network |
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| QIAO Junfei,FU Wentao,HAN Honggui. Dissolved oxygen control method based on self-organizing T-S fuzzy neural network[J]. Journal of Chemical Industry and Engineering(China), 2016, 67(3): 960-966. DOI: 10.11949/j.issn.0438-1157.20151924 |
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| Authors: | QIAO Junfei FU Wentao HAN Honggui |
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| Affiliation: | Beijing Key Laboratory of Computational Intelligence and Intelligent System, College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China |
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| Abstract: | It is difficult to control the dissolved oxygen concentration of the wastewater treatment, a novel approach of control method based on the self-organizing T-S fuzzy neural network (SOTSFNN) is proposed. The essence of the approach according to the actual environment adjust the neuron self-adaptation in time, based on the activity intensity comparisons of the fuzzy rules layer, and construct the appropriate control structure, thus increase the accuracy of control effect. Meanwhile, the parameters of the controller are adjusted on line using error back propagation algorithm. Finally, the controller is applied to Benchmark Simulation Model No.1. The results indicate that the proposed SOTSFNN controller can achieve better control effect for dissolved oxygen concentration with good adaptability. |
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| Keywords: | neural networks control dissolved oxygen concentration dynamic simulation self-organization algorithm |
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