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|Title:||Taguchi-tuned radial basis function with application to high precision motion control||Authors:||Tan, K.K.
|Keywords:||Adaptive and precision motion control
Radial basis function (RBF)
|Issue Date:||Jan-2001||Citation:||Tan, K.K., Tang, K.Z. (2001-01). Taguchi-tuned radial basis function with application to high precision motion control. Artificial Intelligence in Engineering 15 (1) : 25-36. ScholarBank@NUS Repository. https://doi.org/10.1016/S0954-1810(00)00024-8||Abstract:||This paper presents a novel application of Taguchi method to systematically tune the weights of a radial basis function (RBF) network, which is widely used for modelling vaguely defined but smooth nonlinear functions. The main strength of this method is the well-defined and systematic statistical design procedure, which is amenable to practical implementation. To illustrate the effectiveness of the Taguchi-tuned RBF, a test platform is required. This approach is applied to a platform involving high precision motion control. The developed method then is used to tune a composite motion controller incorporating RBF-based adaptive control in a high precision motion environment. Simulation and experimental results reveal the effectiveness of a Taguchi-tuned RBF. © 2001 Elsevier Science Ltd.||Source Title:||Artificial Intelligence in Engineering||URI:||http://scholarbank.nus.edu.sg/handle/10635/57591||ISSN:||09541810||DOI:||10.1016/S0954-1810(00)00024-8|
|Appears in Collections:||Staff Publications|
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