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Fuzzy Broad Learning System Based on Accelerating Amount
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AI
DOI：10.1109/TFUZZ.2021.3128210.png)
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Abstract
En 中文
For taking out the adjustment process of sparse auto-encoder for broad learning system, Feng et al. proposed fuzzy broad learning system by replacing the feature nodes of broad learning system with Takagi-Sugeno fuzzy systems. In fuzzy broad learning system, artificial parameters selection of ridge regression might result in the decrease in testing accuracy. To overcome this shortcoming of fuzzy broad learning system, this article builds a novel model of fuzzy broad learning system based on accelerating amount by introducing the accelerating amount into fuzzy broad learning system. The theoretical result on the universal approximation property of fuzzy broad learning system based on accelerating amount is presented. Three experiment studies on the regression problems of UCI, fashion MNIST, and medical MNIST datasets are performed to show the improvement in testing accuracy.
Keywords:
Learning systems
Training
Fuzzy systems
Testing
Automation
Takagi-Sugeno model
Network architecture
Accelerating amount
fuzzy broad learning system (FBLS)
Takagi-Sugeno (TS) fuzzy systems
universal approximation property
Journal
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11.9
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5.0K
Citations:
2.9W
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