Simulated neurocontrol of an autogenous mill with evolutionary reinforcement learning

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dc.contributor.authorDe Groenewald J.W.V.
dc.contributor.authorAldrich C.
dc.contributor.authorEksteen J.J.
dc.contributor.authorConradie A.V.E.
dc.contributor.authorCoetzer L.P.
dc.date.accessioned2011-10-13T16:59:25Z
dc.date.available2011-10-13T16:59:25Z
dc.date.issued2007
dc.description.abstractIn this investigation the development of nonlinear control system for an autogenous mill was considered. A symbiotic adaptive neuroevolution algorithm was used in conjunction with a dynamic multilayer perceptron model fitted to actual plant data to evolve neurocontrol systems. Simulation studies established the potential of the approach, which yielded satisfactory results, despite having had to learn from a model that covered part of the state space only. Copyright © 2007 IFAC.
dc.description.versionConference Paper
dc.identifier.citationIFAC Proceedings Volumes (IFAC-PapersOnline)
dc.identifier.citation7
dc.identifier.citationPART 1
dc.identifier.citationhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79960955471&partnerID=40&md5=9a5a6400e60efdb179753d8d698eb950
dc.identifier.issn14746670
dc.identifier.urihttp://hdl.handle.net/10019.1/17103
dc.titleSimulated neurocontrol of an autogenous mill with evolutionary reinforcement learning
dc.typeConference Paper
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