Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 30, Issue 9, Pages 4017-4024Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2021.3128210
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
Funding
- National Key Research and Development Program of China [2018YFB1700400]
- National Natural Science Foundation of China [61906015]
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This article introduces a novel model of fuzzy broad learning system based on accelerating amount, aiming to improve the testing accuracy of fuzzy broad learning system.
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.
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