摘要
针对流动电流仪检测自来水浊度的精度受絮凝剂浓度、原水流量、供电电源波动和温度等干扰影响较大的问题,提出一种基于模糊神经网络融合技术的自来水浊度检测数据处理方法。该方法将模糊推理融入神经网络结构中,弥补了纯神经网络在处理模糊数据方面的不足以及纯模糊控制系统在学习方面的缺陷,实现了计算方法的优势互补。仿真结果表明,这种方法能够有效提高自来水浊度检测的精度,在自来水的生产应用中效果良好。
By using streaming current meter,the turbidity detection for tap water is facing the problem of accuracy greatly influenced by the concentration of flocculent,flow rate of raw water,fluctuation of the power supply and temperature.Aiming at this problem,the turbidity detection and data processing method based on fuzzy neural network fusion technology is proposed.In this method,the fuzzy reasoning is integrated into the structure of neural network;this complements the demerit of pure neural network in processing fuzzy data,and the defect of pure fuzzy control system in learning,thus implements mutual complement of the superiorities of the calculation methods.The result of simulation indicates that this method effectively enhances the accuracy of turbidity detection and offers excellent effects in tap water production.
出处
《自动化仪表》
CAS
北大核心
2011年第3期53-56,共4页
Process Automation Instrumentation
关键词
模糊神经网络
电流仪
浊度检测
数据融合
干扰
精度
Fuzzy neural network Current meter Turbidity detection Data fusion Interference Accuracy
