Pages that link to "Item:Q983202"

The following pages link to Identification of nonlinear systems using polynomial nonlinear state space models (Q983202):

Displaying 38 items.

• Optimization on a Grassmann manifold with application to system identification (Q458866) (← links)
• Identification of systems with localised nonlinearity: from state-space to block-structured models (Q490609) (← links)
• Two nonlinear optimization methods for black box identification compared (Q608476) (← links)
• Identification of polyperiodic nonlinear systems (Q671973) (← links)
• Symmetric tensor decomposition by an iterative eigendecomposition algorithm (Q738956) (← links)
• Nonlinear differential equations: Parametric identification by exact polynomial spline schemes (Q1280793) (← links)
• Tensor network subspace identification of polynomial state space models (Q1626879) (← links)
• Iterative identification methods for input nonlinear multivariable systems using the key-term separation principle (Q1660514) (← links)
• Partial orthogonal rank-one decomposition of complex symmetric tensors based on the Takagi factorization (Q1677472) (← links)
• Recursive nonlinear-system identification using latent variables (Q1797027) (← links)
• Nonlinear system identification and adaptive control using polynomial networks (Q1910776) (← links)
• On the smoothness of nonlinear system identification (Q2003795) (← links)
• Online learning of both state and dynamics using ensemble Kalman filters (Q2072638) (← links)
• Variational system identification for nonlinear state-space models (Q2103663) (← links)
• On optimal design of experiments for static polynomial approximation of nonlinear systems (Q2203474) (← links)
• Chaos control in delayed phase space constructed by the Takens embedding theory (Q2204824) (← links)
• Identification of stochastic nonlinear models using optimal estimating functions (Q2207186) (← links)
• On the existence of proper stochastic Markov models for statistical reconstruction and prediction of chaotic time series (Q2213641) (← links)
• Linear prediction error methods for stochastic nonlinear models (Q2280666) (← links)
• A flexible state-space model for learning nonlinear dynamical systems (Q2407186) (← links)
• Structure discrimination in block-oriented models using linear approximations: a theoretic framework (Q2409436) (← links)
• Feedback identification of conductance-based models (Q2662260) (← links)
• Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning (Q2688065) (← links)
• Robust and Nonlinear Control: literature survey (No. 18) (Q2928343) (← links)
• On the Problem of Decoupling Multivariate Polynomials (Q3297698) (← links)
• Identification of a class of nonlinear state-space models using RPE techniques (Q3818966) (← links)
• Grey-box state-space identification of nonlinear mechanical vibrations (Q4570961) (← links)
• Model selection for dynamical systems via sparse regression and information criteria (Q4644829) (← links)
• Efficient parameterisation of nonlinear system models: a comment on Nöel and Schoukens (2018) (Q5134305) (← links)
• Learning latent dynamics for partially observed chaotic systems (Q5139802) (← links)
• Decoupling Multivariate Polynomials Using First-Order Information and Tensor Decompositions (Q5264998) (← links)
• Kernel-based identification of asymptotically stable continuous-time linear dynamical systems (Q5863750) (← links)
• An efficient recursive identification algorithm for multilinear systems based on tensor decomposition (Q6092365) (← links)
• Learning low-dimensional separable decompositions of MIMO non-linear systems (Q6105538) (← links)
• A regularised fast recursive algorithm for fraction model identification of nonlinear dynamic systems (Q6109495) (← links)
• Deep subspace encoders for nonlinear system identification (Q6136161) (← links)
• Identification of polynomial nonlinear systems based on center manifold (Q6537304) (← links)
• Knowledge-based learning of nonlinear dynamics and chaos (Q6562228) (← links)