Self-boosting first-order autonomous learning neuro-fuzzy systems

Xiaowei Gu, Plamen Parvanov Angelov

Research output: Contribution to journalArticlepeer-review

18 Citations (SciVal)
36 Downloads (Pure)

Abstract

In this paper, a detailed mathematical analysis of the optimality of the premise and consequent parts of the recently introduced first-order Autonomous Learning Multi-Model (ALMMo) neuro-fuzzy system is conducted. A novel self-boosting algorithm for structure- and parameter-optimization is, then, introduced to the ALMMo, which results in the self-boosting ALMMo (SBALMMo) neuro-fuzzy system. By minimizing the objective functions with the previously collected data, the SBALMMo is able to optimize its system structure and parameters in few iterations. Numerical examples based benchmark datasets and real-world problems demonstrate the effectiveness and validity of the SBALMMo, and show the strong potential of the proposed approach for real applications.
Original languageEnglish
Pages (from-to)118-134
Number of pages17
JournalApplied Soft Computing
Volume77
Early online date09 Jan 2019
DOIs
Publication statusPublished - 01 Apr 2019
Externally publishedYes

Keywords

  • Autonomous learning
  • Local optimality
  • Neuro-fuzzy systems
  • Self-boosting
  • Streaming data processing

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