基于RBF神经网络的污水处理软测量算法研究  

A soft measure algorithm for sewage disposat based on RBF neural network

作　　者：崔祎[1] 田丽[1] 周峰[1] 李泽应[1] 王军[1] 

机构地区：[1]安徽工程科技学院电气与传动控制重点实验室,安徽芜湖241000

出　　处：《自动化与仪器仪表》2007年第4期3-4,39,共3页Automation & Instrumentation

摘　　要：分析了软测量技术在污水处理问题上的可行性,建立了径向基(RBF)神经网络软测量模型,对污水处理过程中的各种污染物质进行监控和预测。结果表明:应用软测量技术能较好的克服污水处理过程中由随机干扰、强非线性、大时变、严重滞后等因素带来的一系列问题,具有广阔的应用前景。This paper investigates the feasibility of soft measure for sewage disposal, and constructs a soft measure model based on RBF neural network, which is used for inspecting and forecasting diversified pollutants through the process of sewage disposal. The result indicates, the soft measure can overcomes a series of problems through the process of sewage disposal such as random interfere, strong nonlinearity, big time change, serious lag and so on, therefore, this method will has a bright future.

关 键 词：软测量 神经网络 污水处理 预测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]