摘要
提出了一种改进的模糊神经网络软测量建模方法,采用规则化的平均输出隶属度函数作为模糊基函数进行反模糊化运算;在训练网络时,部分参数采用Levenberg-Marquardt算法来训练,另一部分采用一阶梯度下降法。最后用该建模方法建立了聚合反应中熔融指数的软测量模型,并与一般的模糊神经网络软测量模型进行比较。结果表明改进的模糊神经网络对初始值的选择不敏感,具有很好的收敛性,同时还能达到指定的预测精度,很适合工程应用。
A soft sensor modeling algorithm based on improved fuzzy neural network is presented in this paper. The normalized average output membership functions are defined as fuzzy basis functions for defuzzification calculation. In order to improve the convergence , some parameters are trained by Levenberg-Marquardt algorithm, and the others are trained by steepest descent method. Finally, a soft sensor model of melt index in polymer reaction based on the proposed method is presented, and the simulation results show that, in contrast to the traditional fuzzy neural network , the proposed method which is not sensitive to initial parameters and has good convergence performance and prediction precision, is suitable to practical applications.
出处
《信息与控制》
CSCD
北大核心
2003年第4期367-370,共4页
Information and Control
基金
国家杰出青年科学基金(60025308)
浙江省自然科学重点基金(ZD9905)
关键词
模糊神经网络
软测量
建模
隶属度函数
soft sensor
fuzzy neural network
Levenberg-Marquardt algorithm
melt index
