Pages that link to "Item:Q1788772"

The following pages link to Iterative identification algorithm for Wiener nonlinear systems using the Newton method (Q1788772):

Displaying 29 items.

• Iterative parameter identification methods for nonlinear functions (Q450140) (← links)
• Newton iterative algorithm based modeling and proportional derivative controller design for second-order systems (Q500017) (← links)
• Least squares based and gradient based iterative identification for Wiener nonlinear systems (Q548893) (← links)
• EM algorithm-based identification of a class of nonlinear Wiener systems with missing output data (Q748024) (← links)
• Particle swarm optimization iterative identification algorithm and gradient iterative identification algorithm for Wiener systems with colored noise (Q1654314) (← links)
• Gradient-based iterative identification for Wiener nonlinear dynamic systems with moving average noises (Q1736704) (← links)
• Gradient based iterative parameter identification for Wiener nonlinear systems (Q1789549) (← links)
• An iterative numerical algorithm for modeling a class of Wiener nonlinear systems (Q1940691) (← links)
• Robust consensus tracking based on hABC algorithm with parameters identification for uncertain nonlinear FOMASs with external disturbances (Q2068236) (← links)
• The identification of voids and inclusions based on the parameter level set method using boundary data (Q2110790) (← links)
• Parameter estimation of Wiener systems based on the particle swarm iteration and gradient search principle (Q2193618) (← links)
• Parameter identification of fractional order system using enhanced response sensitivity approach (Q2206124) (← links)
• Parameters estimation using mABC algorithm applied to distributed tracking control of unknown nonlinear fractional-order multi-agent systems (Q2207119) (← links)
• Newton iterative identification method for an input nonlinear finite impulse response system with moving average noise using the key variables separation technique (Q2259602) (← links)
• Identification of nonlinear cascade systems with output hysteresis based on the key term separation principle (Q2282652) (← links)
• Identification of Wiener model with internal noise using a cubic spline approximation-Bayesian composite quantile regression algorithm (Q2290958) (← links)
• Gradient-based iterative identification for MISO Wiener nonlinear systems: application to a glutamate fermentation process (Q2339054) (← links)
• Stochastic gradient with changing forgetting factor-based parameter identification for Wiener systems (Q2349241) (← links)
• Application of the Newton iteration algorithm to the parameter estimation for dynamical systems (Q2349538) (← links)
• Least-squares-based iterative identification algorithm for Wiener nonlinear systems (Q2375585) (← links)
• Newton iterative identification for a class of output nonlinear systems with moving average noises (Q2436948) (← links)
• Iterative estimation methods for Hammerstein controlled autoregressive moving average systems based on the key-term separation principle (Q2439375) (← links)
• Iterative parameter estimation algorithms for dual-frequency signal models (Q2633182) (← links)
• Newton-Raphson algorithm for the recursive generalized least-squares identification (Q3753869) (← links)
• Parameter identification of Wiener systems using brain storm optimization algorithm (Q4574691) (← links)
• Multistage for identification of Wiener time delay systems based on hierarchical gradient approach (Q5035719) (← links)
• Parameter identification for Wiener-finite impulse response system with output data of missing completely at random mechanism and time delay (Q6493673) (← links)
• Identification of Wiener systems based on the variable forgetting factor multierror stochastic gradient and the key term separation (Q6495317) (← links)
• Second-order optimization methods for time-delay autoregressive exogenous models: nature gradient descent method and its two modified methods (Q6496765) (← links)