Robust extreme learning fuzzy systems using ridge regression for small and noisy datasets

Te Zhang, Zhaohong Deng, Kup Sze Choi, Jiefang Liu, Shitong Wang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)

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 languageEnglish
Title of host publication2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017
PublisherIEEE
ISBN (Electronic)9781509060344
DOIs
Publication statusPublished - 23 Aug 2017
Event2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 - Royal Continental Hotel, Naples, Italy
Duration: 9 Jul 201712 Jul 2017

Conference

Conference2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017
CountryItaly
CityNaples
Period9/07/1712/07/17

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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