Methodology for developing a neural network leaf spring model

dc.contributor.authorKat, Cor-Jacques
dc.contributor.authorJohrendt, Jennifer L.
dc.contributor.authorEls, Pieter Schalk
dc.contributor.emailschalk.els@eng.up.ac.zaen_ZA
dc.date.accessioned2018-04-06T08:26:17Z
dc.date.available2018-04-06T08:26:17Z
dc.date.issued2017
dc.description.abstractThis paper describes the development of a neural network that is able to emulate the vertical force-displacement behaviour of a leaf spring. Special emphasis is placed on aspects that affect the predictive capability of a neural network such as type, structure, inputs and ability to generalise. These aspects are investigated in order to enable the effective use of it to model leaf spring behaviour. The results show that with the correct selection of inputs and network architecture, the neural network's ability to generalise can be improved and also reduce the required training data. The resulting 2-15-1 feed-forward neural network is shown to generalise well and requires minimal data to be trained. Experimental data was used to train and validate the network. The methodology followed is not limited to the application of leaf springs only but should apply to various other applications especially ones with similar nonlinear characteristics.en_ZA
dc.description.departmentMechanical and Aeronautical Engineeringen_ZA
dc.description.embargo2018-06-01
dc.description.librarianhj2018en_ZA
dc.description.urihttp://www.inderscience.com/jhome.php?jcode=IJVSMTen_ZA
dc.identifier.citationKat, C., Johrendt, J.L. & Els, P.S. 2017, 'Methodology for developing a neural network leaf spring model', International Journal of Vehicle Systems and Testing, vol. 12, no. 1-2, pp. 91-113.en_ZA
dc.identifier.issn1745-6436 (print)
dc.identifier.issn1745-6444 (online)
dc.identifier.other10.1504/IJVSMT.2017.087971
dc.identifier.urihttp://hdl.handle.net/2263/64416
dc.language.isoenen_ZA
dc.publisherInderscienceen_ZA
dc.rights© 2017 Inderscience Enterprises Ltd.en_ZA
dc.subjectLeaf spring modellingen_ZA
dc.subjectMulti-leaf springen_ZA
dc.subjectNeural networksen_ZA
dc.subjectGeneralisationen_ZA
dc.subjectExperimental training dataen_ZA
dc.subjectExperimental validationen_ZA
dc.subject.otherEngineering, built environment and information technology articles SDG-09
dc.subject.otherSDG-09: Industry, innovation and infrastructure
dc.subject.otherEngineering, built environment and information technology articles SDG-12
dc.subject.otherSDG-12: Responsible consumption and production
dc.subject.otherEngineering, built environment and information technology articles SDG-11
dc.subject.otherSDG-11: Sustainable cities and communities
dc.titleMethodology for developing a neural network leaf spring modelen_ZA
dc.typePostprint Articleen_ZA

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