[1]刘福才,陈 超,张彦柳.采样点个数对T-S模糊建模精度的影响[J].智能系统学报,2008,3(6):541-547.
LIU Fu-cai,CHEN Chao,ZHANG Yan-liu.The influence of sampling points on the descriptive performance of TS fuzzy modeling[J].CAAI Transactions on Intelligent Systems,2008,3(6):541-547.
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LIU Fu-cai,CHEN Chao,ZHANG Yan-liu.The influence of sampling points on the descriptive performance of TS fuzzy modeling[J].CAAI Transactions on Intelligent Systems,2008,3(6):541-547.
采样点个数对T-S模糊建模精度的影响
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
3
期数:
2008年第6期
页码:
541-547
栏目:
学术论文—人工智能基础
出版日期:
2008-12-25
- Title:
- The influence of sampling points on the descriptive performance of TS fuzzy modeling
- 文章编号:
- 1673-4785(2008)06-0541-07
- Keywords:
- fuzzy identification; number of sampling points; triangular membership function; netdiagonal method; T-S fuzzy model
- 分类号:
- TP15
- 文献标志码:
- A
- 摘要:
- 在模糊建模中所取的采样点个数会对辨识出的模型精度产生影响，在只给出有限个数据采样点且数据分布不能人为控制的情况下怎样选取最优的采样点个数是模糊辨识中要解决的问题之一.通过采样点个数变化的模糊辨识算法来研究模糊建模中采样点个数对模型描述性能的影响.基于TS模糊模型，采用对称三角形模糊划分和“网格对角线法”提取模糊规则，通过对DISO系统和MackeyGlass 无序时间序列进行建模，给出模糊模型训练性能指标和检验性能指标随采样点个数增加的变化趋势曲线.
- Abstract:
- The number of sampling points in fuzzy modeling has a substantial influence on the accuracy of models. If the sampled data is limited and its distribution not properly controlled, choice of the optimal number of sampling points creates significant problems in fuzzy identification. The author proposed a fuzzy identification algorithm with varied sampling points to investigate the influence of the number of sampling points on descriptive performance. Based on the T-S fuzzy model, we extracted the fuzzy rules by using the symmetrical triangular fuzzy division and the netdiagonal method. By modeling the DISO system and the Mackey Glass chaotic time series, we concluded that training and testing performance indexes in fuzzy models will increase with increased numbers of sampling points.
- 参考文献/References:
-
［1］王立新. 模糊系统: 挑战与机遇并存——十年研究之感悟［J］. 自动化学报, 2001, 27(4):585-590.
WANG Lixin. Fuzzy systems: Challenges and chance—my experiences and perspectives［J］. Acta Automatica Sinica,2001, 27(4):585590.
［2］汤兵勇. 模糊模型的辨识及应用［M］. 北京：中国环境科学出版社, 1994:38-45.
［3］刘福才. 非线性系统的模糊模型辨识及其应用［M］. 北京: 国防工业出版社, 2006.
［4］刘福才,陈?? 超,邵?? 慧，等. 模糊系统万能逼近理论研究综述［J］. 智能系统学报, 2007,2(1):25=34.
LIU Fucai, CHEN Chao, SHAO Hui, et al. The researches for universal approximation of fuzzy systems: A survey［J］. CAAI Transactions on Intelligent Systems, 2007,2(1):25-34.
［5］NOZAKI K, TANAKA H. A simple but powerful heuristic method for generating fuzzy rules from numerical data［J］. Fuzzy Sets Syst，1997, 86(2): 251-270.
［6］岳玉芳, 毛剑琴. 一种基于T-S模型的快速自适应建模方法［J］. 控制与决策, 2002, 17(2):155-158.
YUE Yufang, MAO Jianqin. Fast adaptive modeling based on T-S model［J］. Control and Decision, 2002, 17(2):155-158.
［7］刘福才，关新平，裴??? 润. 基于一种新模糊模型的非线性系统模糊辨识［J］. 控制理论与应用, 2003, 20(1):113-116.
?LIU Fucai, GUAN Xinping, PEI Run. Fuzzy identification based on a new fuzzy model for nonlinear systems［J］. Control Theory & Application, 2003, 20(1):113-116.
［8］TAKAGI T, SUGENO M. Fuzzy identification of systems and its application to modeling and control［J］. IEEE Trans Syst, Man, Cybern,1985, 15(1):116-132.
［9］LO J C,YANG C H. A heuristic errorfeedback learning algorithm for fuzzy modeling［J］. IEEE Trans Syst,Man, Cybern, 1999, 29(6):686-691.
- 相似文献/References:
-
[1]刘楠,刘福才,孟爱文.基于改进PSO和FCM的模糊辨识[J].智能系统学报,2019,14(2):378.[doi:10.11992/tis.201707025]
LIU Nan,LIU Fucai,MENG Aiwen.Fuzzy identification based on improved PSO and FCM[J].CAAI Transactions on Intelligent Systems,2019,14():378.[doi:10.11992/tis.201707025]
备注/Memo
收稿日期：2007-10-31.
基金项目：国家863计划资助项目.
作者简介：
刘福才, 男, 1966年生，教授，博士生导师. 主要研究方向为模糊辨识与预测控制、军事测控技术、电力拖动及其计算机控制等. 发表学术论文120余篇，出版专著1部.
陈???? 超，女, 1982年生，硕士研究生.主要研究方向为模糊辨识与模糊控制.
?张彦柳，女，1984年生，硕士研究生.主要研究方向为模糊建模与图像处理技术.
更新日期/Last Update:
2009-04-06