Pages that link to "Item:Q1996646"

The following pages link to A novel learning algorithm of the neuro-fuzzy based Hammerstein-Wiener model corrupted by process noise (Q1996646):

Displaying 8 items.

• Correlation analysis algorithm-based multiple-input single-output Wiener model with output noise (Q2280271) (← links)
• Decentralized adaptive neuro-fuzzy dynamic surface control for maximum power point tracking of a photovoltaic system (Q2676132) (← links)
• Auxiliary model‐based recursive least squares algorithm for two‐input single‐output Hammerstein output‐error moving average systems by using the hierarchical identification principle (Q6069288) (← links)
• Separation identification approach for the <scp>Hammerstein‐Wiener</scp> nonlinear systems with process noise using correlation analysis (Q6154708) (← links)
• Correlation analysis-based parameter learning of Hammerstein nonlinear systems with output noise (Q6173490) (← links)
• Parameter learning for the nonlinear system described by Hammerstein model with output disturbance (Q6580891) (← links)
• Parameter learning for the nonlinear system described by a class of Hammerstein models (Q6612059) (← links)
• Identification of the Hammerstein nonlinear system with noisy output measurements (Q6631018) (← links)