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

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #90200 Self-boosting first-order autonomous learning neuro-fuzzy systems
sameAs	Self-boosting first-order autonomous learning neuro-fuzzy systems
Title	Self-boosting first-order autonomous learning neuro-fuzzy systems
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2019-04-01
Creator	Plamen Angelov Xiaowei Gu
status	peer reviewed published
Publisher	Elsevier
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.
Is Part Of	Applied Soft Computing https://kar.kent.ac.uk/id/repository
Subject	QA 75 Electronic computers. Computer science
list of authors	https://kar.kent.ac.uk/id/eprint/90200#authors
volume	77
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #90200 Self-boosting first-order autonomous learning neuro-fuzzy systems

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