A novel approach for ANFIS modelling based on Grey system theory for thermal error compensation

Ali M. Abdulshahed, Andrew P. Longstaff, Simon Fletcher

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

5 Citations (Scopus)


The fast and accurate modelling of thermal errors in machining is an important aspect for the implementation of thermal error compensation. This paper presents a novel modelling approach for thermal error compensation on CNC machine tools. The method combines the Adaptive Neuro Fuzzy Inference System (ANFIS) and Grey system theory to predict thermal errors in machining. Instead of following a traditional approach, which utilises original data patterns to construct the ANFIS model, this paper proposes to exploit Accumulation Generation Operation (AGO) to simplify the modelling procedures. AGO, a basis of the Grey system theory, is used to uncover a development tendency so that the features and laws of integration hidden in the chaotic raw data can be sufficiently revealed. AGO properties make it easier for the proposed model to design and predict. According to the simulation results, the proposed model demonstrates stronger prediction power than standard ANFIS model only with minimum number of training samples.

Original languageEnglish
Title of host publication2014 14th UK Workshop on Computational Intelligence, UKCI 2014 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479955381
Publication statusPublished - 17 Oct 2014
Event14th UK Workshop on Computational Intelligence - University of Bradford, Bradford, United Kingdom
Duration: 8 Sep 201410 Sep 2014
Conference number: 14
http://www.computing.brad.ac.uk/ukci2014/ (Link to Conference Website )


Workshop14th UK Workshop on Computational Intelligence
Abbreviated titleUKCI 2014
Country/TerritoryUnited Kingdom
Internet address


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