Robust High-Gain DNN Observer for Nonlinear Stochastic Continuous Time Systems
2007 (English)Report (Other academic)
A class of nonlinear stochastic processes satysfying a "Lipschitz-type strip condition" and supplied by a linear output equation, is considered. Robust asymptotic (high-gain) state estimation for nonlinear stochastic processes via differential neural networks is discussed. A new type learning law for the weight dynamics is suggested. By a stochastic Lyapunov-like analysis (with Ito formula implementation), the stability conditions for the state estimation error as well as for the neural network weights are established. The upper bound for this error is derived. The numerical example, dealing with "module"-type nonlinearities, illustrates the effectiveness of the suggested approach.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2007. , 9 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2803
Nonlinear observers, Dynamic neural networks, Stochastic processes
IdentifiersURN: urn:nbn:se:liu:diva-56129ISRN: LiTH-ISY-R-2803OAI: oai:DiVA.org:liu-56129DiVA: diva2:316970