Modelling uncertainty in production processes using non-singleton fuzzification and fuzzy cognitive maps: a virgin olive oil case studyTools Marchal, P. Cano, Wagner, Christian, Gámez, J. GarcÍa and Gómez, J. Ortega (2016) Modelling uncertainty in production processes using non-singleton fuzzification and fuzzy cognitive maps: a virgin olive oil case study. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016), 24-29 July 2016, Vancouver, Canada. Full text not available from this repository.
Official URL: http://ieeexplore.ieee.org/abstract/document/7737821/
AbstractDecision 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.
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