Pages that link to "Item:Q2400928"

The following pages link to Recursive least squares and multi-innovation stochastic gradient parameter estimation methods for signal modeling (Q2400928):

Displaying 10 items.

• State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors (Q5241000) (← links)
• Multi‐innovation gradient parameter estimation for multivariable systems based on the maximum likelihood principle (Q6053671) (← links)
• Variational Bayesian identification for bilinear state space models with Markov‐switching time delays (Q6061263) (← links)
• Three‐stage forgetting factor stochastic gradient parameter estimation methods for a class of nonlinear systems (Q6061864) (← links)
• Maximum likelihood extended gradient‐based estimation algorithms for the input nonlinear controlled autoregressive moving average system with variable‐gain nonlinearity (Q6068317) (← links)
• Maximum likelihood hierarchical least squares-based iterative identification for dual-rate stochastic systems (Q6493487) (← links)
• Iterative parameter and order identification for fractional-order nonlinear finite impulse response systems using the key term separation (Q6494668) (← links)
• Iterative identification methods for a class of bilinear systems by using the particle filtering technique (Q6494849) (← links)
• Hierarchical recursive least squares algorithms for Hammerstein nonlinear autoregressive output-error systems (Q6495162) (← links)
• Auxiliary model-based multi-innovation recursive identification algorithms for an input nonlinear controlled autoregressive moving average system with variable-gain nonlinearity (Q6495656) (← links)