Deterministic Learning and Rapid Dynamical Pattern Recognition of Discrete-Time Systems
Recently, a deterministic learning theory was proposed for identification and rapid pattern recognition of uncertain nonlinear dynamical systems. In this paper, we investigate deterministic learning of discrete-time nonlinear systems. For periodic or recurrent dynamical patterns, the persistent excitation (PE) condition can be satisfied by a regression subvector constructed from the neurons near the sequence. With the satisfaction of the PE condition, it is shown that the internal dynamics of...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of IEEE International Symposium on Intelligent Control 2008|
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|02_Liu_Deterministic_Learning_and_2008.pdf||822.25 kB||Adobe PDF||Request a copy|
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