基于参数优化的自适应模糊神经网络控制在污水处理中的应用  

Based on the Optimization of Adaptive Fuzzy Neural Network Control in Sewage Treatment

作　　者：张秀玲[1] 郑翠翠[1] 黄兴格[1] 逄宗鹏[1] 

机构地区：[1]燕山大学电气工程学院燕山大学工业计算机控制工程河北省重点实验室,河北秦皇岛066004

出　　处：《化工自动化及仪表》2009年第3期12-14,18,共4页Control and Instruments in Chemical Industry

基　　金：国家自然科学基金资助项目(50675186)

摘　　要：针对活性污泥污水处理系统的机理模型具有复杂的非线性,传统的控制方法存在着精度不高,自适应能力差等缺点,提出一种优化的自适应模糊神经网络控制方法,分析了控制模型参数对系统的影响,并经过数据训练得到控制器各参数的寻优方法,获得系统的最优化参数。该控制方法能够快速、有效地使曝气池中溶解氧浓度达到期望值,并且具有较高的控制效果与控制精度。与传统控制方法相比,该控制方法更具有鲁棒性。仿真结果验证了该控制方法的有效性和正确性。According to the complex non-linear mechanism model of activated sludge sewage treatment system and the shortcomings of low-precision and poor adaptive capacity of traditional methods of control, a fuzzy optimization of the adaptive neural network control method was given. The influence of control model parametersto the system was ana- lyzed. The controller parameters optimization methods were obtained by data trained and the optimized parameters of system were gotten. The control method had good effect and high precision, and could quickly and effectively make the concentration of dissolved oxygen aeration tank meet the expectation value. Compared with traditional control method, this control method had more robustness. The simulation results show the effectiveness and correctness of the control method.

关 键 词：参数优化 模糊神经网络 非线性 鲁棒性 污水处理 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]