Pages that link to "Item:Q493977"
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The following pages link to A nonlinear recursive instrumental variables identification method of Hammerstein ARMAX system (Q493977):
Displaying 13 items.
- Identification of Hammerstein nonlinear ARMAX systems using nonlinear adaptive algorithms (Q493959) (← links)
- Bias compensation principle based recursive least squares identification method for Hammerstein nonlinear systems (Q508338) (← links)
- Filtering based parameter estimation for observer canonical state space systems with colored noise (Q509526) (← links)
- Outlier robust stochastic approximation algorithm for identification of MIMO Hammerstein models (Q1637273) (← links)
- A novel APSO-aided weighted LSSVM method for nonlinear Hammerstein system identification (Q1691186) (← links)
- Recursive maximum likelihood method for the identification of Hammerstein ARMAX system (Q2292362) (← links)
- Gradient-based identification methods for Hammerstein nonlinear ARMAX models (Q2432376) (← links)
- Identification of Hammerstein nonlinear ARMAX systems (Q2576101) (← links)
- Modelling and multi-innovation parameter identification for Hammerstein nonlinear state space systems using the filtering technique (Q2808783) (← links)
- Instrument variable method based on nonlinear transformed instruments for Hammerstein system identification (Q4555759) (← links)
- Maximum likelihood based identification methods for rational models (Q5025848) (← links)
- Performance Analysis of The Auxiliary‐Model‐Based Multi‐Innovation Stochastic Newton Recursive Algorithm for Dual‐Rate Systems (Q5270472) (← links)
- Random dynamic analysis of wind-vehicle-bridge system based on ARMAX surrogate model and high-order differencing (Q6538109) (← links)
