计算机工程 ›› 2010, Vol. 36 ›› Issue (8): 203-205.doi: 10.3969/j.issn.1000-3428.2010.08.071

• 人工智能及识别技术 • 上一篇    下一篇

SBR系统中的模糊神经网络控制器设计

包 枫1,赵鹤鸣2,陈 静3   

  1. (1. 苏州市职业大学机电工程系,苏州 215104;2. 苏州大学电子信息学院,苏州 215000;3. 苏州市职业大学计算机工程系,苏州 215104)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2010-04-20 发布日期:2010-04-20

Design of Fuzzy Neural Network Controller in SBR System

BAO Feng1, ZHAO He-ming2, CHEN Jing3   

  1. (1. Dept. of Mechanic and Electronic Engineering, Suzhou Vocational University, Suzhou 215104; 2. School of Electronic Information, Soochow University, Suzhou 215000; 3. Dept. of Computer Engineering, Suzhou Vocational University, Suzhou 215104)
  • Received:1900-01-01 Revised:1900-01-01 Online:2010-04-20 Published:2010-04-20

摘要: 将模糊控制与神经网络相结合,设计4层模糊神经网络控制器,分析其结构及算法。利用神经网络的自学习能力,在线动态调整模糊变量的隶属函数,优化控制规则,并对曝气池中溶解氧浓度与活性污泥浓度进行控制。通过Matlab对溶解氧的控制进行数字仿真实验,结果表明,具有学习能力的模糊神经网络控制可在污水处理系统的应用中获得更优的性能。

关键词: 模糊神经网络, 智能控制, 序批式活性污泥法, 溶解氧

Abstract: This paper combines fuzzy control with nerve network, designs 4-layers fuzzy neural network controllers, analyzes the structure and algorithm in detail, uses self-study ability of nerve network, on-line and dynamic adjusts the variable of membership function. It optimizes its control rules, and makes the concentration of dissolved oxygen and activated sludge under control. The paper designs fuzzy neural network controllers and respectively applies them to the control of dissolved oxygen, and simulates the fuzzy controllers. Results indicate that the fuzzy neural network controllers with self-study ability are better capability in wastewater treatment system.

Key words: fuzzy neural network, intelligent control, Sequencing Batch Reactor(SBR), Dissolved Oxygen(DO)

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