摘要
闭环系统的辨识是近年来在国内很受重视的研究课题．本文基于闭环对象的历史数据，采用模糊方法构造系统的初始模型，以克服闭环数据稀疏给系统辨识带来的困难，并基于现场数据，采用OLS算法对初始模型进行修正，以提高系统的辨识精度．将这种方法应用于某合成氨过程的实际数据，得到了良好的辨识效果．
There is much interest in closed-loop system identification recently. In this paper, based on historical input and output data,an initial model using fuzzy method is constructed to encounter the difficulty of sparse identification data. The initial model is then enhanced by a radial basis function neural network model trained using input-output data. The new identification method is used in real data from an ammonia process with satisfactory results.
出处
《电子学报》
EI
CAS
CSCD
北大核心
1998年第5期109-112,共4页
Acta Electronica Sinica
基金
国家攀登计划资助
关键词
非线性系统辨识
模糊
神经网络
Non-linear system identification, Fuzzy, Neural networks
