Data-driven design of the extended fuzzy neural network having linguistic outputs
Li, Chengdong1; Ding, Zixiang1; Qian, Dianwei2; Lv, Yisheng3
Source PublicationJOURNAL OF INTELLIGENT & FUZZY SYSTEMS
2018
Volume34Issue:1Pages:349-360
SubtypeArticle
AbstractIn 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.
KeywordData-driven Method Fuzzy Neural Network Multi-objective Optimization Structure Reduction
WOS HeadingsScience & Technology ; Technology
DOI10.3233/JIFS-171348
WOS KeywordWEIGHTED AVERAGE ; SYSTEMS ; PREDICTION ; ALGORITHM ; IDENTIFICATION ; SETS
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61473176 ; 61105077 ; 61573225)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000423039300027
Citation statistics
Cited Times:9[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21948
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
Affiliation1.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
Recommended Citation
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.
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