[1]陈朦,李柠,李少远.室内舒适性指标PMV的区间II型T-S模糊建模[J].智能系统学报,2011,6(03):219-244.
 CHEN Meng,LI Ning,LI Shaoyuan.Interval type2 T-S fuzzy modeling of the PMV thermal comfort index[J].CAAI Transactions on Intelligent Systems,2011,6(03):219-244.
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室内舒适性指标PMV的区间II型T-S模糊建模(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第6卷
期数:
2011年03期
页码:
219-244
栏目:
出版日期:
2011-06-25

文章信息/Info

Title:
Interval type2 T-S fuzzy modeling of the PMV thermal comfort index
文章编号:
1673-4785(2011)03-0219-06
作者:
陈朦12李柠12李少远12
1.上海交通大学 自动化系,上海 200240;
2.上海交通大学 系统控制与信息处理教育部重点实验室,上海 200240
Author(s):
CHEN Meng12 LI Ning12 LI Shaoyuan12
1. Department of Automation, Shanghai JiaoTong University, Shanghai 200240, China;
2. Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai 200240, China
关键词:
PMVCFDII型模糊模型区间TS模糊系统
Keywords:
predicted mean vote computational fluid dynamics type2 fuzzy modeling interval T-S fuzzy systems
分类号:
TP273.4
文献标志码:
A
摘要:
预测平均投票值(PMV)是室内热环境的标准化指标,其涉及的数学模型复杂且存在不确定性,不能适应实时控制的需要.同时,传统的采用一个PMV值评价热环境的方法具有局限性,不能反映不同位置人体舒适感的差异.为了处理测量噪声和人体因素带来的不确定性,通过对室内气流和传热计算流体动力学(CFD)模拟数值以准确描述PMV值,建立了PMV的区间II型TS模糊模型.针对二阶模糊隶属度的确定问题,在GK聚类的基础上,采用遗传算法对二阶隶属度函数的参数进行优选,再由最小二乘法辨识得到后件参数.仿真结果表明II型TS 模糊模型比I型更有效地减少了不确定性,对模型精度的影响,对动态过程和稳态数值都有很好的预测能力.
Abstract:
The predicted mean vote (PMV) index is widely used to evaluate the indoor thermal comfort with indoor environmental and human factors considered. However, PMV is difficult to control realtimely as its mathematical model is complicated and uncertain. Moreover, spatial distributions of environmental factors are neglected by using one PMV index in a room. In this paper, computational fluid dynamics (CFD) technology was applied for simulation of the environmental factors in order to accurately describe the PMV index. To deal with measurement noises or other system uncertainties, an interval type2 fuzzy model of PMV was developed and a new GKGAbased modeling method was proposed. The essential issue of type2 fuzzy modeling lies in the appropriate choice of secondary membership functions. In this study, the primary membership function was gained through a GustafsonKessel (GK) algorithm, and the secondary membership function was determined through a genetic algorithm (GA). The consequent parameter of the fuzzy rules was identified by a least squared algorithm. Simulation results show that the type2 fuzzy model is superior to type1 fuzzy model in minimizing the influence of uncertainties. The proposed method is effective and accurate. 

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2010-11-18.
基金项目:国家自然科学基金资助项目(60934007,61074061);上海市科委基础研究重点资助项目(10JC1403400).
通信作者:李柠. E-mail: ning_li@sjtu.edu.cn.
 作者简介:

陈朦,男,1986年生,硕士研究生,主要研究方向为模糊建模与控制.
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、智能控制等.先后承担并参加国家自然科学基金、国家“863”计划、上海市科研项目等10余项.2006年获得上海市自然科学一等奖(第三完成人).发表学术论文50余篇,其中被SCI、EI检索30余篇.
李少远,男,1965年生,教授,博士生导师,上海市自动化学会理事长,中国自动化学会控制理论专业委员会委员.主要研究方向为预测控制、自适应智能控制等.承担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.2000年获得上海市教委“曙光学者”称号,2004年进入教育部新世纪优秀人才支持计划,2006年获得上海市自然科学一等奖(第一完成人),2008年获得国家杰出青年基金.发表学术论文180余篇,其中被SCI、EI检索100余篇.
更新日期/Last Update: 2011-07-23