期刊
IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS
卷 150, 期 3, 页码 311-316出版社
IEE-INST ELEC ENG
DOI: 10.1049/ip-cta:20030204
关键词
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In general, neural networks cannot exactly represent nonlinear systems. A neuro identifier has to include robust modification in order to guarantee Lyapunov stability. An input-to-state stability approach is used to create robust training algorithms for discrete-time neural networks. It is concluded that the gradient descent law and a backpropagation-type algorithm used for the weight adjustments are stable in the sense of L-infinity and robust to any bounded uncertainties.
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