基于模糊-神经网络的pH控制  

The pH Control Based on Fuzzy Neural Network

作　　者：熊红霞[1] 崔峰[1] 李锡文[1] 

机构地区：[1]华中科技大学机械科学与工程学院,湖北武汉430074

出　　处：《机械与电子》2013年第4期41-43,46,共4页Machinery & Electronics

摘　　要：提出了模糊-神经网络的pH控制方法,利用神经网络对非线性发酵系统进行模型辨识,模拟实际发酵罐;采用模糊控制算法,以发酵罐内pH的偏差和偏差变化率作为输入,蠕动泵加入的碱的量作为输出。仿真结果表明,pH值稳定在6.2±0.05,达到控制精度。The accurate and precise modes are hardly built due to the complexity and variance of ferment process, hence a new method of pH control based on fuzzy neural network is put forward in this dissertation. Ferment model identification is made to simulate the actual ferment cylinder taking advantage of the approach ability of neural network to nonlinear system. The simulating system＇s in- puts are deviation and deviation rate of pH;it＇s output is the quantity of feeding alkali using fuzzy algorithm. The simulating results show that the ferment pH is stable at 6.20.05,meeting the ac-curacy of expectation.

关 键 词：发酵 PH 模糊控制 神经网络 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]