Please use this identifier to cite or link to this item:
|Title:||Identification of piecewise affine systems and nonlinear systems using multiple models|
|Citation:||Lai, C.Y., Xiang, C., Lee, T.H. (2010). Identification of piecewise affine systems and nonlinear systems using multiple models. 2010 8th IEEE International Conference on Control and Automation, ICCA 2010 : 2005-2012. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCA.2010.5524281|
|Abstract:||In this paper, a procedure for the identification of piecewise affine ARX systems is proposed. The parameters of the individual subsystems are identified through a least-squares based identification method using multiple models. The partition of the regressor space is then determined using standard procedures such as neural network classifier or support vector machine classifier. The same procedure can be applied to identify nonlinear systems by approximating them via piecewise affine systems. Extensive simulation studies show that our algorithm can indeed provide accurate estimates of the plant parameters even in noisy cases, and even when the model orders are overestimated. © 2010 IEEE.|
|Source Title:||2010 8th IEEE International Conference on Control and Automation, ICCA 2010|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Aug 13, 2018
checked on Aug 17, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.