Neural nets for the simulation of mineral processing operations: Part I. Theoretical principles

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dc.contributor.authorVan Der Walt T.J.
dc.contributor.authorVan Deventer J.S.J.
dc.contributor.authorBarnard E.
dc.date.accessioned2011-05-15T15:54:00Z
dc.date.available2011-05-15T15:54:00Z
dc.date.issued1993
dc.description.abstractThe ill-defined nature of processes in the metallurgical industry necessitates the quest for new modelling techniques to emulate features of processes which are poorly understood from a fundamental point of view. For this reason nonparametric regression techniques such as neural nets offer an appealing alternative to fundamental modelling. The robust associative and computational properties of neural networks make these regression tools ideally suited for the modelling of ill-defined systems. Being the most commonly-used connectionist network, sigmoidal backpropagation neural networks (SBNN's) have been shown to model metallurgical and chemical systems satisfactorily wothout any a prioir knowledge about the system provided sufficient data are available. This paper introduces the field of connectionists networks to the metallurgical process engineer and describes the fundamentals of an SBNN. © 1993.
dc.description.versionArticle
dc.identifier.citationMinerals Engineering
dc.identifier.citation6
dc.identifier.citation11
dc.identifier.issn8926875
dc.identifier.urihttp://hdl.handle.net/10019.1/8936
dc.titleNeural nets for the simulation of mineral processing operations: Part I. Theoretical principles
dc.typeArticle
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