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

dc.creatorSalmeron J.L.
dc.creatorRuiz-Celma A.
dc.creatorMena A.
dc.date.accessioned2020-09-02T22:27:27Z
dc.date.accessioned2022-11-08T20:21:21Z
dc.date.available2020-09-02T22:27:27Z
dc.date.available2022-11-08T20:21:21Z
dc.date.created2020-09-02T22:27:27Z
dc.date.issued2017
dc.identifier232, , 52-57
dc.identifier09252312
dc.identifierhttps://hdl.handle.net/20.500.12728/6072
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5143986
dc.languageen
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


Este ítem pertenece a la siguiente institución