[1]廖倩芳,李? 柠,李少远.一种数据驱动的Ⅱ型T-S模糊建模方法[J].智能系统学报,2009,4(4):303-308.
　LIAO Qian-fang,LI Ning,LI Shao-yuan.A TypeⅡ TS fuzzy modeling method for datadriven approaches[J].CAAI Transactions on Intelligent Systems,2009,4(4):303-308.
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一种数据驱动的Ⅱ型T-S模糊建模方法

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 4 期数: 2009年第4期 页码: 303-308 栏目: 学术论文—人工智能基础 出版日期: 2009-08-25
Title:
A TypeⅡ TS fuzzy modeling method for datadriven approaches
文章编号:
1673-4785(2009)04-0303-06
作者:
廖倩芳，李? 柠，李少远
上海交通大学电子信息与电气工程学院，上海 200240
Author(s):
LIAO Qian-fang, LI Ning, LI Shao-yuan
chool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
关键词:
Ⅱ型模糊; TS模糊模型; 数据驱动; pH中和反应过程
Keywords:
TypeⅡ fuzzy;  TS fuzzy model;  datadriven;  pH neutralization process
分类号:
TP273
文献标志码:
A
摘要:
现场采集的数据不可避免地包含一些诸如噪声干扰之类的不确定性，由数据驱动建立的模型需要具备较强的处理不确定因素影响的能力.在以往文献的Ⅰ型TS模糊建模方法的基础上，提出了一种基于数据驱动的Ⅱ型TS模糊建模方法.其过程是通过分析采集的数据样本计算得到不确定因素的影响程度，在Ⅰ型TS模糊模型的基础上，前件参数上采用Ⅱ型的模糊集来代替Ⅰ型的模糊集，后件参数上则采用I型模糊集来代替数值，由此拓展得到Ⅱ型TS模糊模型.最后通过pH中和反应过程对所提出的方法进行仿真验证.仿真结果表明，该方法建立的模型能更好地处理不确定因素的影响，取得更高的准确度.
Abstract:
Data collected from the field inevitably contains uncertainties such as noise or other disturbances; mathematical models established with datadriven approach must possess strong capability to deal with the influence of uncertainties. Following analysis of current methods for typeI TakagiSugeno (TS) fuzzy modeling, a method suitable for typeⅡ TS fuzzy modeling was proposed. In the data driven modeling process, the influence of the degree of uncertainty was determined by analysis of the collected data. On the basis of the typeI fuzzy model, for antecedent parameters, we employed the fuzzy set of the typeⅡ fuzzy model to replace the counterpart from the typeⅠ model . But for consequent parameters, we took typeI fuzzy sets to replace crisp numbers. This produced an improved typeⅡ TS fuzzy model. Finally, a pH neutralization process was taken as an example to verify the proposed mathematical model. Simulation results showed that this method can handle the influence of uncertainties better and achieves higher accuracy.
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备注/Memo

收稿日期：2008-10-24.
基金项目：国家自然科学基金资助项目(60604018 )；上海自然科学基金资助项目(06ZR14044).
通信作者：李 柠.E-mail: ning_li@sjtu.edu.cn.
作者简介：
廖倩芳，女，1983年生，硕士研究生，主要研究方向为Ⅱ型模糊建模与控制.?
李 柠，女，1974年生，副研究员，硕士生导师，主要研究方向为复杂系统建模与控制、预测控制等.
李少远，男，1965年生，教授，博士生导师，主要研究方向为预测控制、自适应智能控制等.2006年获得上海市自然科学一等奖(第一完成人).承担了包括国家自然科学基金、国家“863”计划在内的国家级科研项目10余项.在国内外学术杂志上发表学术论文180余篇，其中被SCI和EI检索100余篇.

更新日期/Last Update: 2009-11-16
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