Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification
文献类型：期刊论文
| 作者 | Feng, Shuangg3,4; Chen, C. L. Philip1,2,4 |
| 刊名 | IEEE TRANSACTIONS ON CYBERNETICS
 |
| 出版日期 | 2020-02-01 |
| 卷号 | 50期号:2页码:414-424 |
| 关键词 | Broad learning system (BLS) classification k-means regression Takagi-Sugeno (TS) fuzzy system |
| ISSN号 | 2168-2267 |
| DOI | 10.1109/TCYB.2018.2857815 |
| 通讯作者 | Chen, C. L. Philip(philip.chen@ieee.org) |
| 英文摘要 | A novel neuro-fuzzy model named fuzzy broad learning system (BLS) is proposed by merging the Takagi-Sugeno (TS) fuzzy system into BLS. The fuzzy BLS replaces the feature nodes of BLS with a group of TS fuzzy subsystems, and the input data are processed by each of them. Instead of aggregating the outputs of fuzzy rules produced by every fuzzy subsystem into one value immediately, all of them are sent to the enhancement layer for further nonlinear transformation to preserve the characteristic of inputs. The defuzzification outputs of all fuzzy subsystem and the outputs of enhancement layer are combined together to obtain the model output. The k-means method is employed to determine the centers of Gaussian membership functions in antecedent part and the number of fuzzy rules. The parameters that need to be calculated in a fuzzy BLS are the weights connecting the outputs of enhancement layer to model output and the randomly initialized coefficients of polynomials in consequent part in fuzzy subsystems, which can be calculated analytically. Therefore, fuzzy BLS retains the fast computational nature of BLS. The proposed fuzzy BLS is evaluated by some popular benchmarks for regression and classification, and compared with some state-of-the-art nonfuzzy and neuro-fuzzy approaches. The results indicate that fuzzy BLS outperforms other models involved. Moreover, fuzzy BLS shows advantages over neuro-fuzzy models regarding to the number of fuzzy rules and training time, which can ease the problem of rule explosion to some extent. |
| WOS关键词 | RESTRICTED BOLTZMANN MACHINE ; INFERENCE SYSTEM ; NETWORKS ; RULE ; IDENTIFICATION ; APPROXIMATION ; ALGORITHM ; EQUIVALENCE ; CONTROLLER |
| 资助项目 | National Natural Science Foundation of China[61751202] ; National Natural Science Foundation of China[61751205] ; National Natural Science Foundation of China[61572540] ; Macau Science and Technology Development Fund (FDCT)[019/2015/A1] ; Macau Science and Technology Development Fund (FDCT)[079/2017/A2] ; Macau Science and Technology Development Fund (FDCT)[024/2015/AMJ] ; University of Macau ; Teacher Research Capacity Promotion Program of Beijing Normal University, Zhuhai |
| WOS研究方向 | Automation & Control Systems ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000506849800002 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; Macau Science and Technology Development Fund (FDCT) ; University of Macau ; Teacher Research Capacity Promotion Program of Beijing Normal University, Zhuhai |
| 源URL | [http://ir.ia.ac.cn/handle/173211/29513]  |
| 专题 | 离退休人员 |
| 通讯作者 | Chen, C. L. Philip |
| 作者单位 | 1.Dalian Maritime Univ, Coll Nav, Dalian 116026, Peoples R China 2.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100080, Peoples R China 3.Beijing Normal Univ, Sch Appl Math, Zhuhai Campus, Zhuhai 519085, Peoples R China 4.Univ Macau, Fac Sci & Technol, Macau 99999, Peoples R China |
| 推荐引用方式 GB/T 7714 | Feng, Shuangg,Chen, C. L. Philip. Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification[J]. IEEE TRANSACTIONS ON CYBERNETICS,2020,50(2):414-424. |
| APA | Feng, Shuangg,&Chen, C. L. Philip.(2020).Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification.IEEE TRANSACTIONS ON CYBERNETICS,50(2),414-424. |
| MLA | Feng, Shuangg,et al."Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification".IEEE TRANSACTIONS ON CYBERNETICS 50.2(2020):414-424. |
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。