Modelling twofold uncertainty in the condition assessment of residential buildings using interval valued fuzzy signatures

Adam Bukovics, Istvan A. Harmati, Laszlo T. Koczy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this study we will describe an intervention decision-support method related to residential buildings, which is modelling the condition of buildings based on linguistic expert opinions so that it is capable of taking into consideration the uncertainties included in the expert opinions. Fuzzy signature-based model is used, wherein the uncertainties are integrated into the system by applying interval valued fuzzy sets, as well as linguistic hedges. The result will be a fuzzy set for each building, describing the condition of the whole building, specify the bourdary values between which the condition of the building may fall.

Original languageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509042401
DOIs
Publication statusPublished - Feb 9 2017
Event2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Athens, Greece
Duration: Dec 6 2016Dec 9 2016

Other

Other2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
CountryGreece
CityAthens
Period12/6/1612/9/16

Keywords

  • assessment
  • interval valued fuzzy signature
  • membership function
  • residential building
  • type-2 method

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems and Management
  • Control and Optimization
  • Artificial Intelligence

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    Bukovics, A., Harmati, I. A., & Koczy, L. T. (2017). Modelling twofold uncertainty in the condition assessment of residential buildings using interval valued fuzzy signatures. In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 [7849994] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2016.7849994