Please use this identifier to cite or link to this item: 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
Source: 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
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