LoboVault Home
 

A Learning Algorithm for Applying Cohen's Models to System Identification

LoboVault

Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/20209

A Learning Algorithm for Applying Cohen's Models to System Identification

Show full item record

Title: A Learning Algorithm for Applying Cohen's Models to System Identification
Author: Abdallah, Chaouki T.; Howes, James W.; Heileman, Gregory L.
Abstract: Abstract In this paper we extend the models discussed by Cohen (1992) by introducing an input term. This allows the resulting models to be utilized for system identification tasks. We prove that this model is stable in the sense that a bounded input leads to a bounded state when a minor restriction is imposed on the Lyapunov function. By employing this stability result, we are able to find a learning algorithm which guarantees convergence to a set of parameters for which the error between the model trajectories and the desired trajectories vanish.
Date: 2012-03-24
URI: http://hdl.handle.net/1928/20209

Files in this item

Files Size Format View
Abdallah Howse ... System Identification.pdf 335.2Kb PDF View/Open

This item appears in the following Collection(s)

Show full item record

UNM Libraries

Search LoboVault


Advanced Search

Browse

My Account