CIVILICA We Respect the Science
Publisher of Iranian Journals and Conference Proceedings
Paper
title

An adaptive-network-based fuzzy inference system (ANFIS) for prediction of characteristics in the Mechanics of Composite Materials

Credit to Download: 1 | Page Numbers 6 | Abstract Views: 61
Year: 2019
COI code: MMICONF01_006
Paper Language: English

How to Download This Paper

For Downloading the Fulltext of CIVILICA papers please visit the orginal Persian Section of website.

Authors An adaptive-network-based fuzzy inference system (ANFIS) for prediction of characteristics in the Mechanics of Composite Materials

  Hadi Mehdipour - Department of Mechanical Engineering, Faculty of Shahid Chamran, Kerman Branch, Technical and Vocational University (TVU), Kerman, Iran

Abstract:

Lack of actual amount of principal factors in the composite materials filed results in imprecision and uncertainty. Use of different approaches for analyzing and solving the engineering problems depends on nature and amount of uncertainty of problems.When the information of the system is characterized through linguistic terms in such situations, the fuzzy theory can be used to determine the structural response in the sense of evaluation of its upper and lower bounds, respectively. We can obtain a more strong fuzzy system by combining neural network techniques and fuzzy logic called Adaptive Fuzzy Systems (AFS).In this article, we propose a new neuro-fuzzy technique for the composite materials, in order to find an optimal volume fraction when Hooke’s law is utilized in a unidirectional lamina. We devised an Adaptive Network-based Fuzzy Inference System (ANFIS) as an estimator system for composite materials science. To construct a neuro-fuzzy system has utilized a Takagi-Sugeno-Kang type fuzzy system. We show that the ANFIS is more accurate rather than a rule of mixture theory in the estimation of empirical data especially in uncertain situations. In order to evaluate the proposed approach, we perform experiments on a dataset of empirical data through MATLAB software. Wecompared the ability of the proposed approach with the rule of mixtures approach and illustrated that ANFIS is more accurate to estimate the empirical data.

Keywords:

Composite materials; ANFIS; Fuzzy logic; rule of mixture

Perma Link

https://www.civilica.com/Paper-MMICONF01-MMICONF01_006.html
COI code: MMICONF01_006

how to cite to this paper:

If you want to refer to this article in your research, you can easily use the following in the resources and references section:
Mehdipour, Hadi, 2019, An adaptive-network-based fuzzy inference system (ANFIS) for prediction of characteristics in the Mechanics of Composite Materials, First International Conference on Mechanical, Manufacturing, Industrial and Civil Engineering, استانبول, همايش آروين البرز, https://www.civilica.com/Paper-MMICONF01-MMICONF01_006.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Mehdipour, Hadi, 2019)
Second and more: (Mehdipour, 2019)
For a complete overview of how to citation please review the following CIVILICA Guide (Citation)

Scientometrics

The University/Research Center Information:
Type: Azad University
Paper No.: 6755
in University Ranking and Scientometrics the Iranian universities and research centers are evaluated based on scientific papers.

Research Info Management

Export Citation info of this paper to research management softwares

New Related Papers

Iran Scientific Advertisment Netword

Share this paper

WHAT IS COI?

COI is a national code dedicated to all Iranian Conference and Journal Papers. the COI of each paper can be verified online.