Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.neucom.2008.03.013
Title: Neural network learning algorithm for a class of interconnected nonlinear systems
Authors: Huang, S.N. 
Tan, K.K. 
Lee, T.H. 
Keywords: Adaptive control
Neural network learning
Nonlinear systems
Issue Date: Jan-2009
Source: Huang, S.N., Tan, K.K., Lee, T.H. (2009-01). Neural network learning algorithm for a class of interconnected nonlinear systems. Neurocomputing 72 (4-6) : 1071-1077. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neucom.2008.03.013
Abstract: In this paper, an adaptive neural network algorithm is developed for a class of interconnected nonlinear systems. Neural networks (NNs) are used to approximate the unknown nonlinear functions and interconnections in the subsystems. A systematic approach is established to synthesize the adaptive NN learning control scheme that ensures the boundedness of all the signals in the closed-loop system. The effectiveness of the proposed scheme is demonstrated by computer simulations. © 2008 Elsevier B.V. All rights reserved.
Source Title: Neurocomputing
URI: http://scholarbank.nus.edu.sg/handle/10635/56783
ISSN: 09252312
DOI: 10.1016/j.neucom.2008.03.013
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