Abstract
Fuzzy Extreme Learning Machine (F-ELM) constructs a fuzzy neural networks by embedding fuzzy membership functions and rules into the hidden layer of extreme learning machine (ELM), that is, it can be interpreted as a fuzzy system with the structure of neural network. Although F-ELM has shown the characteristics of fast learning of model parameters, it has poor robustness to small and noisy datasets since its parameters connecting hidden layer with output layer are optimized by least square(LS). In order to overcome this challenge, a Ridge Regression based Extreme Learning Fuzzy System (RR-EL-FS) is presented in this study, which has introduced the strategy of ridge regression into F-ELM to enhance the robustness. The experimental results also validate that the performance of RR-EL-FS is better than F-ELM and some related methods to small and noisy datasets.
Original language | English |
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Title of host publication | 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 |
Publisher | IEEE |
ISBN (Electronic) | 9781509060344 |
DOIs | |
Publication status | Published - 23 Aug 2017 |
Event | 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 - Royal Continental Hotel, Naples, Italy Duration: 9 Jul 2017 → 12 Jul 2017 |
Conference
Conference | 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 |
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Country/Territory | Italy |
City | Naples |
Period | 9/07/17 → 12/07/17 |
ASJC Scopus subject areas
- Software
- Theoretical Computer Science
- Artificial Intelligence
- Applied Mathematics