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Learning FCMs with multi-local and balanced memetic algorithms for forecasting industrial drying processes

dc.contributor.authorSalmeron J.L.
dc.contributor.authorRuiz-Celma A.
dc.contributor.authorMena A.
dc.date.accessioned2020-09-02T22:27:27Z
dc.date.available2020-09-02T22:27:27Z
dc.date.issued2017
dc.identifier10.1016/j.neucom.2016.10.070
dc.identifier.citation232, , 52-57
dc.identifier.issn09252312
dc.identifier.urihttps://hdl.handle.net/20.500.12728/6072
dc.descriptionIn this paper, we propose a Fuzzy Cognitive Map (FCM) learning approach with a multi-local search in balanced memetic algorithms for forecasting industrial drying processes. The first contribution of this paper is to propose a FCM model by an Evolutionary Algorithm (EA), but the resulted FCM model is improved by a multi-local and balanced local search algorithm. Memetic algorithms can be tuned with different local search strategies (CMA-ES, SW, SSW and Simplex) and the balance of the effort between global and local search. To do this, we applied the proposed approach to the forecasting of moisture loss in industrial drying process. The thermal drying process is a relevant one used in many industrial processes such as food industry, biofuels production, detergents and dyes in powder production, pharmaceutical industry, reprography applications, textile industries, and others. This research also shows that exploration of the search space is more relevant than finding local optima in the FCM models tested. © 2016 Elsevier B.V.
dc.language.isoen
dc.publisherElsevier B.V.
dc.subjectCognitive MapsMachine learning
dc.subjectFuzzy
dc.subjectIndustrial drying
dc.subjectMemetic algorithm
dc.subjectCognitive systems
dc.subjectDrying
dc.subjectForecasting
dc.subjectLearning algorithms
dc.subjectLocal search (optimization)
dc.subjectSoaps (detergents)
dc.subjectTextile industry
dc.subjectThermal processing (foods)
dc.subjectCognitive MapsMachine learning
dc.subjectFuzzy
dc.subjectFuzzy cognitive map
dc.subjectIndustrial processs
dc.subjectLocal search algorithm
dc.subjectLocal search strategy
dc.subjectMemetic algorithms
dc.subjectPharmaceutical industry
dc.subjectEvolutionary algorithms
dc.subjectdetergent
dc.subjectdye
dc.subjectalgorithm
dc.subjectArticle
dc.subjectbiofuel production
dc.subjectcontrolled study
dc.subjectdrug industry
dc.subjectevolutionary algorithm
dc.subjectfood industry
dc.subjectfuzzy cognitive map
dc.subjectfuzzy system
dc.subjectindustrial drying process
dc.subjectindustry
dc.subjectmemetic algorithm
dc.subjectmoisture
dc.subjectpowder
dc.subjectprocess monitoring
dc.subjectsludge
dc.subjecttextile industry
dc.subjectthermal drying process
dc.titleLearning FCMs with multi-local and balanced memetic algorithms for forecasting industrial drying processes
dc.typeArticle


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