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|Title:||Further result on a dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems||Authors:||Huang, S.N.
|Issue Date:||Dec-2005||Citation:||Huang, S.N., Tan, K.K., Lee, T.H. (2005-12). Further result on a dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems. Automatica 41 (12) : 2161-2162. ScholarBank@NUS Repository. https://doi.org/10.1016/j.automatica.2005.07.003||Abstract:||In Kim et al. [(1997) A dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems. Automatica 33(8), 1539-1543], authors present an excellent neural network (NN) observer for a class of nonlinear systems. However, the output error equation in their paper is strictly positive real (SPR) which is restrictive assumption for nonlinear systems. In this note, by introducing a vector b0 and Lyapunov equation, the observer design is obtained without requiring the SPR condition. Thus, our observer can be applied to a wider class of systems. © 2005 Elsevier Ltd. All rights reserved.||Source Title:||Automatica||URI:||http://scholarbank.nus.edu.sg/handle/10635/56104||ISSN:||00051098||DOI:||10.1016/j.automatica.2005.07.003|
|Appears in Collections:||Staff Publications|
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