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Research Article

Comparing transfer matrix method and ANFIS in free vibration analysis of Timoshenko columns with attachments 


Oktay Demirdag, Bulent Yildirim*

Pamukkale University, Turkey Department of Mechanical Engineering, TURKEY

Keywords

Abstract


Transfer Matrix Method

Adaptive Network Based Fuzzy Inference System

Natural frequency

Timoshenko column




In this study, two approaches having different characteristics, one being Transfer Matrix Method (TMM) that reduces computational effort and time by reducing the dimension of the considered matrix to four for all problems and the other being The Adaptive Network based Fuzzy Inference System (ANFIS) used in The Fuzzy Logic Toolbox of Matlab software that again needs less computational effort and time are compared in the free vibration analysis of Timoshenko columns with attached masses having rotary inertia. The governing equation of the column elements is solved by applying the separation of variables method in the TMM algorithm. The same problems are solved, also, by fuzzy-neural approach in which ANFIS model is used by establishing Neuro Fuzzy Frequency Estimation (NFFE) models. Natural frequencies for the first three modes of an elastically supported Timoshenko column with 1, 5 and 10 attached masses are computed using NFFE models, and the results are compared with the ones of TMM. The comparison graphs are presented in numerical analysis to show the effectiveness of the considered methods, and it is resulted that neuro-fuzzy approach may give encouraging results for these kinds of models having great number of attached masses.

© 2015 MIM Research Group. All rights reserved.

DOI: http://dx.doi.org/10.17515/resm2015.12me0814                                Full Text

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2022 Reviewer Awards:

Please, visit Reviewer Awards section for the winners of the 2022 RESM reviewer awards.


2022 Best Paper Award:

The paper authored by Nitin Kumar, Michele Barbato, Erika L. Rengifo-López and Fabio Matta entitled as “Capabilities and limitations of existing finite element simplified micro-modeling techniques for unreinforced masonry” is awarded the 


2022 Most Cited Paper Award:

The paper authored by Aykut Elmas, Güliz Akyüz, Ayhan Bergal, Müberra Andaç and Ömer Andaç entitled as “Mathematical modelling of drug release" is awarded the


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