TY - GEN
T1 - New switched filtering method for recurrent neural networks
AU - Ahn, Choon Ki
PY - 2011
Y1 - 2011
N2 - In this paper, we propose a new robust filtering method for switched neural networks via input/output-to-state stability (IOSS) approach. This robust filtering method guarantees that the filtering error system is asymptotically stable and input/output-to-state stable for the external disturbance. The unknown gain matrix of the proposed filter can be obtained by solving a set of linear matrix inequalities (LMIs), which can be easily facilitated by using some standard numerical packages.
AB - In this paper, we propose a new robust filtering method for switched neural networks via input/output-to-state stability (IOSS) approach. This robust filtering method guarantees that the filtering error system is asymptotically stable and input/output-to-state stable for the external disturbance. The unknown gain matrix of the proposed filter can be obtained by solving a set of linear matrix inequalities (LMIs), which can be easily facilitated by using some standard numerical packages.
KW - input/output-to-state stability
KW - robust filtering
KW - switched neural networks
UR - http://www.scopus.com/inward/record.url?scp=80053117946&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80053117946&partnerID=8YFLogxK
U2 - 10.1109/URKE.2011.6007842
DO - 10.1109/URKE.2011.6007842
M3 - Conference contribution
AN - SCOPUS:80053117946
SN - 9781424499830
T3 - Proceedings of the International Conference on Uncertainty Reasoning and Knowledge Engineering, URKE 2011
SP - 71
EP - 74
BT - Proceedings of the International Conference on Uncertainty Reasoning and Knowledge Engineering, URKE 2011
T2 - 2011 International Conference on Uncertainty Reasoning and Knowledge Engineering, URKE 2011
Y2 - 4 August 2011 through 7 August 2011
ER -