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Type-2 Fuzzy Broad Learning System
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AI
DOI：10.1109/TCYB.2021.3070578.png)
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Abstract
En 中文
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.
Keywords:
Neurons
Uncertainty
Robustness
Learning systems
Nonlinear systems
Convergence
Standards
Broad learning system (BLS)
fuzzy pseudoinverse learning (FPL) algorithm
interval type-2 fuzzy neuron
robustness
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W