Self-tuning method for fuzzy rule base with belief structure

Jun Liu, Luis Martinez, Jian Bo Yang, Jin Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A framework for modelling the safety of an engineering system using a fuzzy rule-based evidential reasoning (FURBER) approach has been proposed recently, where a fuzzy rule-base designed on the basis of a belief structure (called a belief rule expression matrix) forms a basis in the inference mechanism of FURBER. In this paper, a learning method for optimally training the elements of the belief rule expression matrix and other knowledge representation parameters in FURBER is proposed. This process is formulated as a nonlinear objective function to minimize the differences between the output of a belief rule base and given data. The optimization problem is solved using the optimization tool provided in MATLAB. A numerical example is provided to demonstrate how the method can be implemented.

Original languageEnglish
Title of host publicationApplied Computational Intelligence - Proceedings of the 6th International FLINS Conference
PublisherWorld Scientific Publishing Co. Pte Ltd
Pages272-275
Number of pages4
ISBN (Print)9812388737, 9789812388735
DOIs
Publication statusPublished - 2004
EventApplied Computational Intelligence - Proceedings of the 6th International FLINS Conference - Blankenberge, Belgium
Duration: 1 Sep 20043 Sep 2004

Publication series

NameApplied Computational Intelligence - Proceedings of the 6th International FLINS Conference

Conference

ConferenceApplied Computational Intelligence - Proceedings of the 6th International FLINS Conference
CountryBelgium
CityBlankenberge
Period1/09/043/09/04

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