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Nonlinear observer design using dynamic recurrent neural networks

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/20376

Nonlinear observer design using dynamic recurrent neural networks

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Title: Nonlinear observer design using dynamic recurrent neural networks
Author: Abdallah, Chaouki T.; Kim, Young H.; Lewis, Frank L.
Subject(s): Chaos
Erbium
Nonlinear systems
Abstract: A nonlinear observer for a general class of single-output nonlinear systems is proposed based on a generalized dynamic recurrent neural network (DRNN). The neural network (NN) weights in the observer are tuned online, with no off-line learning phase required. The observer stability and boundness of the state estimates and NN weights are proven. No exact knowledge of the nonlinear function in the observed system is required. Furthermore, no linearity with respect to the unknown system parameters is assumed. The proposed DRNN observer can be considered as a universal and reusable nonlinear observer because the same observer can be applied to any system in the class of nonlinear systems.
Date: 1996-12-11
Publisher: IEEE
Citation: Proceedings of the 35th IEEE Decision and Control, 1996, 1: 949-954
Description: Digital Object Identifier : 10.1109/CDC.1996.574590
URI: http://hdl.handle.net/1928/20376
ISBN: 0-7803-3590-2

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