中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 先进控制与自动化团队
Data-driven design of the extended fuzzy neural network having linguistic outputs

文献类型：期刊论文

作者Li, Chengdong1; Ding, Zixiang1; Qian, Dianwei2; Lv, Yisheng3
刊名JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
出版日期2018
卷号34期号:1页码:349-360
关键词Data-driven Method Fuzzy Neural Network Multi-objective Optimization Structure Reduction
DOI10.3233/JIFS-171348
文献子类Article
英文摘要In many data-driven modeling, prediction or identification applications to unknown systems, linguistic (fuzzy) results described by fuzzy sets are more preferable than the crisp results described by numbers owing to the uncertainties and/or noises existed in the observed data. On the other hand, fuzzy neural network (FNN) provides a powerful tool for providing accurate crisp results, but does not have the ability to achieve linguistic outputs due to its crisp weights. This study extends the crisp weights of FNN to fuzzy ones to obtain linguistic outputs. And, a data-driven design method is proposed to construct this kind of fuzzily weighted FNN (FW-FNN). The proposed data-driven method includes four steps. Firstly, a fully connected FNN is generated. Then, the SVD-QR method based pruning strategy is presented to realize the structure reduction of the initial FW-FNN. Thirdly, the centers of the Gaussian fuzzy weights in the structure reduced FW-FNN are learned by the least square method. Fourthly, the multi-objective algorithm is utilized to optimize the widths of the Gaussian fuzzy weights to achieve the maximum of the average membership grades of the output fuzzy sets and the minimum of the coverage intervals of the linguistic outputs. To evaluate the proposed FW-FNN and the data-driven method, applications to the nonlinear dynamic system identification, the chaotic time series prediction and the traffic flow prediction are given. Simulation results demonstrate that the linguistic outputs can effectively capture the uncertainties and/or noises in the observed data. It provides us a very useful tool for system modeling, prediction and identification especially when uncertainties and/or noises should be taken into account.
WOS关键词WEIGHTED AVERAGE ; SYSTEMS ; PREDICTION ; ALGORITHM ; IDENTIFICATION ; SETS
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000423039300027
资助机构National Natural Science Foundation of China(61473176 ; 61105077 ; 61573225)
源URL[http://ir.ia.ac.cn/handle/173211/21948]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
作者单位1.Shandong Jianzhu Univ, Sch Informat & Elect Engn, Jinan 250101, Shandong, Peoples R China
2.North China Elect Power Univ, Sch Control & Comp Engn, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Li, Chengdong,Ding, Zixiang,Qian, Dianwei,et al. Data-driven design of the extended fuzzy neural network having linguistic outputs[J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS,2018,34(1):349-360.
APA Li, Chengdong,Ding, Zixiang,Qian, Dianwei,&Lv, Yisheng.(2018).Data-driven design of the extended fuzzy neural network having linguistic outputs.JOURNAL OF INTELLIGENT & FUZZY SYSTEMS,34(1),349-360.
MLA Li, Chengdong,et al."Data-driven design of the extended fuzzy neural network having linguistic outputs".JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 34.1(2018):349-360.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。