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

Xiaowei Gu, Plamen Parvanov Angelov

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

18 Dyfyniadau (Scopus)
38 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

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.
Iaith wreiddiolSaesneg
Tudalennau (o-i)118-134
Nifer y tudalennau17
CyfnodolynApplied Soft Computing
Cyfrol77
Dyddiad ar-lein cynnar09 Ion 2019
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 01 Ebr 2019
Cyhoeddwyd yn allanolIe

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