A novel type-2 fuzzy membership function: Application to the prediction of noisy data

Mojtaba Ahmadieh Khanesar, Mohammad Teshnehlab, Erdal Kayacan, Okyay Kaynak

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

A novel, diamond-shaped type-2 fuzzy membership function is introduced in this study. The proposed type-2 fuzzy membership function has certain values on 0 and 1, but it has some uncertainties for the other membership values. It has been shown that the type-2 fuzzy system using this type of membership function introduced in this study has some noise reduction property in the presence of noisy inputs. The appropriate parameter selection to be able to achieve noise reduction property is also considered. A hybrid method consisting of particle swarm optimization (PSO) and gradient descent (GD) algorithm is used to optimize the parameters of the proposed type-2 fuzzy system. PSO is a derivative-free optimizer, and the possibility of the entrapment of this optimizer in local minimums is less than the gradient descent method. The proposed type-2 fuzzy system and the hybrid parameter estimation method are then tested on the prediction of a noisy, chaotic dynamical system. The simulation results show that the type-2 fuzzy predictor with the proposed novel membership functions shows a superior performance when compared to the other existing type-2 fuzzy systems in the presence of noisy inputs.

Original languageEnglish
Title of host publicationCIMSA 2010 - IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Proceedings
Number of pages6
Publication date2010
Pages128-133
Article number5611774
ISBN (Print)9781424472291
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event8th IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2010 - Taranto, Italy
Duration: 6 Sep 20108 Sep 2010

Conference

Conference8th IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2010
CountryItaly
CityTaranto
Period06/09/201008/09/2010
SponsorIEEE, IEEE, IEEE

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