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https://doi.org/10.1016/S0954-1810(00)00024-8
Title: | Taguchi-tuned radial basis function with application to high precision motion control | Authors: | Tan, K.K. Tang, K.Z. |
Keywords: | Adaptive and precision motion control Nonlinear control Radial basis function (RBF) Taguchi method |
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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