Type-2 Fuzzy Broad Learning System

被引：31
作者：
Han, Honggui [1 ,2 ]
Liu, Zheng [1 ,2 ]
Liu, Hongxu [1 ,2 ]
Qiao, Junfei [1 ,2 ]
Chen, C. L. Philip [3 ,4 ]
机构：
[1] Beijing Univ Technol, Fac Informat Technol, Beijing Key Lab Computat Intelligence & Intellige, Engn Res Ctr Digital Community,Minist Educ,Beijin, Beijing 100124, Peoples R China
[2] Beijing Univ Technol, Beijing Lab Urban Mass Transit, Beijing 100124, Peoples R China
[3] Univ Macau, Fac Sci & Technol, Macau 99999, Peoples R China
[4] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China
来源：
IEEE TRANSACTIONS ON CYBERNETICS | 2022年 / 52卷 / 10期
基金：
北京市自然科学基金; 美国国家科学基金会;
关键词：
Neurons; Uncertainty; Robustness; Learning systems; Nonlinear systems; Convergence; Standards; Broad learning system (BLS); fuzzy pseudoinverse learning (FPL) algorithm; interval type-2 fuzzy neuron; robustness; NEURAL-NETWORK; IDENTIFICATION; PREDICTION; FRAMEWORK; DESIGN;
D O I：
10.1109/TCYB.2021.3070578
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The broad learning system (BLS) has been identified as an important research topic in machine learning. However, the typical BLS suffers from poor robustness for uncertainties because of its characteristic of the deterministic representation. To overcome this problem, a type-2 fuzzy BLS (FBLS) is designed and analyzed in this article. First, a group of interval type-2 fuzzy neurons was used to replace the feature neurons of BLS. Then, the representation of BLS can be improved to obtain good robustness. Second, a fuzzy pseudoinverse learning algorithm was designed to adjust the parameter of type-2 FBLS. Then, the proposed type-2 FBLS was able to maintain the fast computational nature of BLS. Third, a theoretical analysis on the convergence of type-2 FBLS was given to show the computational efficiency. Finally, some benchmark and practical problems were used to test the merits of type-2 FBLS. The experimental results indicated that the proposed type-2 FBLS can achieve outstanding performance.
引用
收藏
页码：10352 / 10363
页数：12
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