A Composite Neural Gas-Elman Network that Captures Real-World Elastic Behavior of 3D Objects

TitleA Composite Neural Gas-Elman Network that Captures Real-World Elastic Behavior of 3D Objects
Publication TypeConference Paper
Year of Publication2006
AuthorsCretu, A. - M., J. Lang, and E. M. Petriu
Conference NameInstrumentation and Measurement Technology Conference, 2006. IMTC 2006. Proceedings of the IEEE
Pagination1063 -1068
Date Publishedapr.
Keywords3D object geometry, compliance measurement, compressed model, computational geometry, deformable objects, elastic deformation, Elman neural network, force-displacement behavior, model acquisition, neural gas network, neural nets, nonlinear elastic behaviors, self-organizing architecture, solid modelling, viscoelastic model

This paper employs a neural gas network to obtain a compressed model of 3D geometry of objects, which accounts for elastic behavior as well. Based on the output of the network, we are able to cluster the object into areas of similar geometry and elasticity and then represent the elastic behavior of each cluster by an Elman neural network that models force-displacement behavior without explicit computation of elastic parameters. This approach allows us not only to recover the elastic parameters in the sampled points (those points for which we have measurements) but also provides us with an estimate on the elastic behavior in points that are not part of the sampling point set. The comparison of the Elman network with the three-element viscoelastic model indicates that the neural approach estimates better nonlinear elastic behaviors than its counterpart


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