Improved PDF technology based NFM for batch process

doi:10.11949/j.issn.0438-1157.20151922

CIESC Journal ›› 2016, Vol. 67 ›› Issue (3): 998-1007.DOI: 10.11949/j.issn.0438-1157.20151922

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Improved PDF technology based NFM for batch process

FU Zhao, JIA Li

Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China

Received:2015-12-17 Revised:2015-12-19 Online:2016-01-12 Published:2016-03-05
Contact: 67
Supported by:
supported by the National Natural Science Foundation of China (61374044), the Shanghai Science Technology Commission (12510709400), the Shanghai Municipal Education Commission (14ZZ088) and the Shanghai Talent Development Plan 2013.

基于改进PDF技术的间歇过程NFM模型

付钊, 贾立

上海大学机电工程与自动化学院自动化系, 上海市电站自动化重点实验室, 上海 200072

通讯作者: 贾立
基金资助:
国家自然科学基金项目(61374044);上海市科委国际合作项目(12510709400);上海市教委创新重点项目(14ZZ088);2013年度上海市人才发展基金项目。

Abstract

Abstract:

Batch process is an typical nolinear production process and can be simulated by a neuro-fuzzy model (NFM). In the previous research, a new model training method called PDF technology was proposed to successfully conquer the weak generalization ability which caused by the MSE rule based model training. But the density function is hard to estimate and the trained model are not stable when the target PDF can not given. To solve these problems, a new window width estimation method is introduced and also a contraction strategy with a PDF predictor is proposed when the target can not be given. Simulation results demonstrate that the proposed methods can get a more accurate density estimation and a more excellent model prediction ability.

Key words: batch process, neuro-fuzzy model, probability density function, contraction strategy, algorithm, prediction

摘要：

间歇过程是一类具有典型复杂非线性特性的生产过程,可以利用模糊神经网络(NFM)建立其输入输出的非线性映射关系。在前期的研究中曾提出过基于概率密度函数(PDF)技术的模型训练方法,成功解决了传统的基于MSE准则训练方法模型泛化能力弱的问题,但又产生了概率密度难以估计及目标PDF未知时模型性能不稳定的问题。针对这两个问题,引入了新的概率密度窗宽估计方法,并提出了在目标PDF未知时采用PDF预估器及其收缩策略的算法。仿真实验表明:该方法能够保证足够的概率密度估计精度和模型预测性能。

关键词: 间歇过程, 神经模糊模型, 概率密度函数, 收缩策略, 算法, 预测

CLC Number:

TP273

FU Zhao, JIA Li. Improved PDF technology based NFM for batch process[J]. CIESC Journal, 2016, 67(3): 998-1007.

付钊, 贾立. 基于改进PDF技术的间歇过程NFM模型[J]. 化工学报, 2016, 67(3): 998-1007.

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Improved PDF technology based NFM for batch process

基于改进PDF技术的间歇过程NFM模型

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