caa a22 4500 445361328 CHVBK 20180317142916.0 cr unu---uuuuu 170323e20110901xx s 000 0 eng 10.1007/s11071-010-9901-5 doi (NATIONALLICENCE)springer-10.1007/s11071-010-9901-5 Ahn Choon Department of Automotive Engineering, Seoul National University of Science & Technology, Seoul, South Korea aut Robust stability of recurrent neural networks with ISS learning algorithm [Elektronische Daten] [Choon Ahn] In this paper, an input-to-state stability (ISS) approach is used to derive a new robust weight learning algorithm for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI) formulation, the ISS learning algorithm is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the design of the ISS learning algorithm can be achieved by solving LMI, which can be easily facilitated by using some standard numerical packages. A numerical example is presented to demonstrate the validity of the proposed learning algorithm. Springer Science+Business Media B.V., 2010 Input-to-state stability (ISS) approach nationallicence Weight learning algorithm nationallicence Dynamic neural networks nationallicence Linear matrix inequality (LMI) nationallicence Nonlinear Dynamics Springer Netherlands 65/4(2011-09-01), 413-419 0924-090X 65:4<413 2011 65 11071 https://doi.org/10.1007/s11071-010-9901-5 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11071-010-9901-5 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Ahn Choon Department of Automotive Engineering, Seoul National University of Science & Technology, Seoul, South Korea aut NATIONALLICENCE 773 0- Nonlinear Dynamics Springer Netherlands 65/4(2011-09-01), 413-419 0924-090X 65:4<413 2011 65 11071 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer