Maximum likelihood based multi-innovation stochastic gradient identification algorithms for bilinear stochastic systems with ARMA noise

Cites Work

Unnamed Item
Performance analysis of multi-innovation gradient type identification methods
Hierarchical gradient- and least squares-based iterative algorithms for input nonlinear output-error systems using the key term separation
Hierarchical recursive generalized extended least squares estimation algorithms for a class of nonlinear stochastic systems with colored noise
Recursive least squares and multi-innovation gradient estimation algorithms for bilinear stochastic systems
Maximum likelihood‐based gradient estimation for multivariable nonlinear systems using the multiinnovation identification theory
Two‐stage auxiliary model gradient‐based iterative algorithm for the input nonlinear controlled autoregressive system with variable‐gain nonlinearity
Separable multi‐innovation stochastic gradient estimation algorithm for the nonlinear dynamic responses of systems
Hierarchical parameter and state estimation for bilinear systems
A shift in paradigm for system identification
State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors
The filtering‐based maximum likelihood iterative estimation algorithms for a special class of nonlinear systems with autoregressive moving average noise using the hierarchical identification principle
Maximum likelihood extended gradient‐based estimation algorithms for the input nonlinear controlled autoregressive moving average system with variable‐gain nonlinearity
Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems
Least squares parameter estimation and multi-innovation least squares methods for linear fitting problems from noisy data

This page was built for publication: Maximum likelihood based multi-innovation stochastic gradient identification algorithms for bilinear stochastic systems with ARMA noise