摘要
为了建立工业污水pH值中和系统的正模型,研究了具有大滞后非线性特性的加药中和过程。利用一种动态自适应最近邻聚类(DANNC)学习算法,全面调整网络参数完成了污水pH值加药中和控制系统网络的学习和训练。采用中和过程神经网络内模控制系统的逆模型充当控制器,进行了各种工业条件下污水中和的仿真实验。结果表明,该系统实现了△pH≤0.2的工业污水的控制精度目标,系统实时跟踪和抗干扰性良好。
In order to establish a positive model of pH value control, the process with severe non-linearity and serious lag of neutralization action was studied by adding medicine in an industrial waster water neutralization control system. A novel kind of Dynamic Adaptive Nearest Neighbor Clustering （DANNC） algorithm was adopted, and a strategy by adjusting the parameter in the entire neural network to finish the task of learning and training of the neural network （NN） was applied. The NN internal model control system for pH value of neutralization, which serves as a controller of the converse model was designed, and different kinds of simulation experiments were carried. The results showed that the accuracy of the pH control system is △pH≤0.2, which satisfied the requirement of the real time adding medicine track and anti-jamming abilities in industrial application.
出处
《北京科技大学学报》
EI
CAS
CSCD
北大核心
2006年第1期84-87,共4页
Journal of University of Science and Technology Beijing
基金
国家自然科学基金(No.60472095)
关键词
工业污水
最近邻聚类学习算法
动态自适应调整
PH值
内模控制
industrial waster water
nearest neighbor clustering algorithm
dynamic self-adapting
pH value
internal model control
