Radial basis function (RBF) for non-linear dynamic system identification
One of the key problem in system identification is finding a suitable model structure. In this paper, radial basis function (RBF) network using various basis functions are trained to represent discrete-time nonlinear dynamic systems and the results are compared. The orthogonal least squarealgorithm...
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| வடிவமà¯: | கடà¯à®Ÿà¯à®°à¯ˆ |
| மொழி: | English |
| வெளியீடபà¯à®ªà®Ÿà¯à®Ÿà®¤à¯: |
Penerbit UTM Press
2002
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| பகà¯à®¤à®¿à®•à®³à¯: | |
| நிகழà¯à®¨à®¿à®²à¯ˆ அணà¯à®•à®²à¯: | http://eprints.utm.my/1301/ http://eprints.utm.my/1301/ http://eprints.utm.my/1301/1/JT36A4.pdf |
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| தொகà¯à®ªà¯à®ªà¯: | One of the key problem in system identification is finding a suitable model structure. In this paper, radial basis function (RBF) network using various basis functions are trained to represent discrete-time nonlinear dynamic systems and the results are compared. The orthogonal least squarealgorithm is employed to select parsimonious RBF models. To demonstrate the identification procedure two examples of modelling on linear system were included. |
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