Stabilization of stochastic recurrent neural networks via inverse optimal control

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)22_Publication in policy or professional journal

6 Scopus Citations
View graph of relations


Related Research Unit(s)


Original languageEnglish
Pages (from-to)1762-1763
Journal / PublicationProceedings of the IEEE Conference on Decision and Control
Publication statusPublished - 2002


Title41st IEEE Conference on Decision and Control
PlaceUnited States
CityLas Vegas, NV
Period10 - 13 December 2002


The paper studies the stabilization problem for a dynamic neural network disturbed by additive noise. The stabilization is achieved from the inverse optimal control approach, recently introduced in nonlinear control theory, using a quadratic Lyapunov function. A simple feedback control law is derived, which ensures that the neural network state is globally asymptotically stable in probability.

Research Area(s)

  • Inverse optimal control, Lyapunov function, Neural networks, Nonlinear systems, Stochastic systems