On robust input design for nonlinear dynamical models
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Publication:510130
DOI10.1016/j.automatica.2016.11.030zbMath1355.93198OpenAlexW2575519021MaRDI QIDQ510130
Patricio E. Valenzuela, Cristian R. Rojas, Thomas B. Schön, Johan Dahlin
Publication date: 16 February 2017
Published in: Automatica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.automatica.2016.11.030
Applications of graph theory (05C90) Nonlinear systems in control theory (93C10) Identification in stochastic control theory (93E12)
Uses Software
Cites Work
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- System identification of nonlinear state-space models
- Sequential Monte Carlo smoothing for general state space hidden Markov models
- Input design for structured nonlinear system identification
- Stationarity on finite strings and shift register sequences
- Dynamic system identification. Experiment design and data analysis
- Uncertain convex programs: randomized solutions and confidence levels
- An adaptive method for consistent estimation of real-valued non-minimum phase zeros in stable LTI systems
- A graph theoretical approach to input design for identification of nonlinear dynamical models
- Inference in hidden Markov models.
- Identification of ARX systems with non-stationary inputs -- asymptotic analysis with application to adaptive input design
- Robust optimal experiment design for system identification
- Sequential Monte Carlo Methods in Practice
- Backward Simulation Methods for Monte Carlo Statistical Inference
- The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs
- Sequential Monte Carlo Samplers
- The EM Algorithm and Extensions, 2E
- A new method for evaluating the log-likelihood gradient, the Hessian, and the Fisher information matrix for linear dynamic systems
- Identification For Control: Optimal Input Design With Respect To A Worst-Case $\nu$-gap Cost Function
- Filtering via Simulation: Auxiliary Particle Filters
- Finding All the Elementary Circuits of a Directed Graph
- Input design via LMIs admitting frequency-wise model specifications in confidence regions
- Robustness in Experiment Design
- Depth-First Search and Linear Graph Algorithms