Please use this identifier to cite or link to this item:
http://dspace.uniten.edu.my/jspui/handle/123456789/7948
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Nordin, F.H. | |
dc.contributor.author | Nagi, F.H. | |
dc.date.accessioned | 2018-02-15T02:15:38Z | - |
dc.date.available | 2018-02-15T02:15:38Z | - |
dc.date.issued | 2008 | |
dc.identifier.uri | http://dspace.uniten.edu.my/jspui/handle/123456789/7948 | - |
dc.description.abstract | Layer-Recurrent Network (LRN) is a dynamic neural network and is seen as a promising black box model in identifying a nonlinear system injected with nonlinear input signal. In this paper, LRN will be used to identify a nonlinear, state space 3-axis satellite model. Open loop identification is applied and methodology on nonlinear system identification is presented where the best pair of input and output data is first measured. Using the simulated data, six LRN models are used to identify the satellite dynamics. It is shown that only 200 epochs are needed to train a network to converge to a reasonable mean squared value (mse). LRN output is then compared with the state space model where it shows that LRN model is capable to produce similar results as the state space satellite model without knowing the system's state and prior knowledge of the system. | |
dc.title | Layer-recurrent network in identifying a nonlinear system | |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
Appears in Collections: | COE Scholarly Publication |
Google ScholarTM
Check
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.