Data-driven design of the extended fuzzy neural network having linguistic outputs
Li, Chengdong1; Ding, Zixiang1; Qian, Dianwei2; Lv, Yisheng3
2018
发表期刊JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
卷号34期号:1页码:349-360
文章类型Article
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.
关键词Data-driven Method Fuzzy Neural Network Multi-objective Optimization Structure Reduction
WOS标题词Science & Technology ; Technology
DOI10.3233/JIFS-171348
关键词[WOS]WEIGHTED AVERAGE ; SYSTEMS ; PREDICTION ; ALGORITHM ; IDENTIFICATION ; SETS
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61473176 ; 61105077 ; 61573225)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000423039300027
引用统计
被引频次:3[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/21948
专题复杂系统管理与控制国家重点实验室_先进控制与自动化
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
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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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