Design of hybrid genetic- SVD ANFIS networks for fatigue life modelling of GRP composites

K. Salmalian (), N. Nariman-Zadeh, H. Haftchenari and A. Jamali
Additional contact information
K. Salmalian: The University of Guilan
N. Nariman-Zadeh: The University of Guilan
H. Haftchenari: The University of Guilan
A. Jamali: The University of Guilan

Fuzzy Information and Engineering, 2009, vol. 1, issue 3, 271-287

Abstract: Abstract Genetic algorithm (GA) and singular value decomposition (SVD) are deployed for the optimal design of both Gaussian membership functions of antecedents and the vector of linear coefficients of consequents, respectively, of adaptive neurofuzzy inference systems (ANFIS) networks that are used for fatigue life modelling and prediction of unidirectional GRP Composites. The aim of such modelling is to show how the fatigue life varies with the variation of important parameters namely, maximum stress, stress ratio, fiber angle. It is demonstrated that SVD can be effectively used to optimally find the vector of linear coefficients of conclusion parts in ANFIS models and their Gaussian membership functions in premise parts are determined by GA.

Keywords: ANFIS; Fatigue life; GRP composites; Genetic algorithms; SVD (search for similar items in EconPapers)
Date: 2009
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12543-009-0021-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:fuzinf:v:1:y:2009:i:3:d:10.1007_s12543-009-0021-1

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/12543

DOI: 10.1007/s12543-009-0021-1

Access Statistics for this article

More articles in Fuzzy Information and Engineering from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().