Dissolved oxygen control method based on self-organizing T-S fuzzy neural network

QIAO Junfei; FU Wentao; HAN Honggui

doi:10.11949/j.issn.0438-1157.20151924

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Dissolved oxygen control method based on self-organizing T-S fuzzy neural network

更新时间：2025-12-10

- Dissolved oxygen control method based on self-organizing T-S fuzzy neural network
- CIESC Journal Issue 3, Pages: 960-966(2016)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20151924
  CLC： P183
- Online First：12 January 2016，
  
  Published：12 January 2016
- 稿件说明：
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QIAO Junfei, FU Wentao, HAN Honggui. Dissolved oxygen control method based on self-organizing T-S fuzzy neural network[J]. CIESC Journal, 2016, (3): 960-966.
DOI：

QIAO Junfei, FU Wentao, HAN Honggui. Dissolved oxygen control method based on self-organizing T-S fuzzy neural network[J]. CIESC Journal, 2016, (3): 960-966. DOI： 10.11949/j.issn.0438-1157.20151924.

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。

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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