Modelling uncertainty in production processes using non-singleton fuzzification and fuzzy cognitive maps: a virgin olive oil case study

Marchal, P. Cano; Wagner, Christian; G�mez, J. Garc�a; G�mez, J. Ortega

Abstract

Decision support systems (DSSs) are a convenient tool to aid plant operators in the selection of process set points. Inputs to these systems for variables that are not easily measured online often come from assessments made by experts, with an associated degree of uncertainty. The application of fuzzy sets and systems as part of DSSs provides a systematic approach to addressing the uncertainty in its variables. This paper builds on prior work on DSSs utilising fuzzy cognitive maps and introduces a non-singleton fuzzification stage which directly addresses uncertainty in system inputs. The motivation of the proposed system is grounded in the real world challenges of producing high-quality olive oil and the paper provides promising application and analysis results as part of the Virgin Olive Oil Production Process.

Citation

Marchal, P. C., Wagner, C., Gámez, J. G., & Gómez, J. O. (in press). Modelling uncertainty in production processes using non-singleton fuzzification and fuzzy cognitive maps: a virgin olive oil case study.

• Conference Name: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016)
• End Date: Jul 29, 2016
• Acceptance Date: Jul 1, 2016
• Online Publication Date: Nov 10, 2016
• Deposit Date: Aug 4, 2017
• Publicly Available Date: Aug 4, 2017
• Peer Reviewed: Peer Reviewed
• Keywords: nonsingleton fuzzification, fuzzy cognitive maps, fuzzy sets
• Public URL: https://nottingham-repository.worktribe.com/output/829267
• Publisher URL: http://ieeexplore.ieee.org/abstract/document/7737821/
• Contract Date: Aug 4, 2017