Monte Carlo filters for identification of nonlinear structural dynamical systems

DOI10.1007/BF02716784zbMath1154.93039MaRDI QIDQ949160

B. E. Eshmatov

Publication date: 20 October 2008

Published in: Sādhanā (Search for Journal in Brave)

zbMATH Keywords

stochastic differential equationsparticle filtersnonlinear structural system identification

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Signal detection and filtering (aspects of stochastic processes) (60G35) Identification in stochastic control theory (93E12) Applications of stochastic analysis (to PDEs, etc.) (60H30)

Related Items (7)

System identification application using Hammerstein model ⋮ Nonlinear filters for chaotic oscillatory systems ⋮ Self-regularized pseudo time-marching schemes for structural system identification with static measurements ⋮ Extended Kalman filters using explicit and derivative-free local linearizations ⋮ A sequential importance sampling filter with a new proposal distribution for state and parameter estimation of nonlinear dynamical systems ⋮ The use of polynomial chaos for parameter identification from measurements in nonlinear dynamical systems ⋮ New forms of extended Kalman filter via transversal linearization and applications to structural system identification

Cites Work

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• A survey of numerical methods for stochastic differential equations
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• Sequential Monte Carlo Methods for Dynamic Systems
• Filtering via Simulation: Auxiliary Particle Filters
• Kalman Filtering
• A survey of convergence results on particle filtering methods for practitioners

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