摘要
提出了利用多模型融合技术进行发酵过程建模的新方法, 该方法能够将在线参数和离线参数同时用于建模中. 首先给出了多模型融合建模算法框架, 并描述了基于自适应模糊神经网络和模糊推理技术两个参与融合的子模型的建立方法. 采用三个非线性函数分别运用GMDH PTSV算法、傅里叶神经网络和多模型融合建模算法进行建模精度比较. 最后给出了多模型融合建模算法在青霉素发酵过程中应用的结果.
A new algorithm, which is based on multi model fusion technique to construct the model of fermentation process, is proposed. The algorithm can simultaneously use on line and off line parameters to construct models. Firstly, multi model fusion modeling algorithms are designed, the modeling method of two sub models based on adaptive fuzzy neural networks and fuzzy inference are described. Secondly, for three nonlinear testing functions, GMDH PTSV, Fourier neural network and multi model fusion modeling algorithms, are used to construct models respectively. Finally, results of the multi model fusion modeling algorithms for penicillin fermentation process are given.
出处
《信息与控制》
CSCD
北大核心
2005年第2期172-176,187,共6页
Information and Control
关键词
数据融合
自适应模糊神经网络
模糊推理
发酵过程
建模
data fusion
adaptive fuzzy neural network
fuzzy inference
fermentation process
modeling
