Pages that link to "Item:Q1614327"

The following pages link to A blind approach to the Hammerstein-Wiener model identification (Q1614327):

Displaying 50 items.

• The recursive least squares identification algorithm for a class of Wiener nonlinear systems (Q285760) (← links)
• Recursive least squares parameter estimation for a class of output nonlinear systems based on the model decomposition (Q318520) (← links)
• Hierarchical least squares algorithms for nonlinear feedback system modeling (Q328119) (← links)
• Identification of smooth nonlinear dynamical systems with non-smooth steady-state features (Q463890) (← links)
• A nonlinear recursive instrumental variables identification method of Hammerstein ARMAX system (Q493977) (← links)
• Decomposition-based least squares parameter estimation algorithm for input nonlinear systems using the key term separation technique (Q494773) (← links)
• Parametric identification of structured nonlinear systems (Q534286) (← links)
• Convergence of the iterative algorithm for a general Hammerstein system identification (Q620607) (← links)
• Spectral inversion of second order Volterra models based on the blind identification of Wiener models (Q634870) (← links)
• MINLIP for the identification of monotone Wiener systems (Q642647) (← links)
• Hierarchical least squares identification for Hammerstein nonlinear controlled autoregressive systems (Q736348) (← links)
• Auxiliary model-based least-squares identification methods for Hammerstein output-error systems (Q876374) (← links)
• Parameter bounds evaluation of Wiener models with noninvertible polynomial nonlinearities (Q880356) (← links)
• Parameter estimation error bounds for Hammerstein nonlinear finite impulsive response models (Q942365) (← links)
• An adaptive nonlinear filter for system identification (Q965395) (← links)
• Towards identification of Wiener systems with the least amount of a priori information: IIR cases (Q1023361) (← links)
• Frequency domain identification of Wiener models (Q1400927) (← links)
• Decoupling the linear and nonlinear parts in Hammerstein model identification (Q1433069) (← links)
• A hierarchical least squares identification algorithm for Hammerstein nonlinear systems using the key term separation (Q1643235) (← links)
• Identification of nonlinear dynamic systems with input saturation and output backlash using three-block cascade models (Q1660378) (← links)
• Identification of block-oriented nonlinear systems starting from linear approximations: a survey (Q1679865) (← links)
• A novel APSO-aided weighted LSSVM method for nonlinear Hammerstein system identification (Q1691186) (← links)
• Extended stochastic gradient identification method for Hammerstein model based on approximate least absolute deviation (Q1793825) (← links)
• Hammerstein-Wiener system estimator initialization (Q1881194) (← links)
• Identification of Hammerstein-Wiener models (Q1939605) (← links)
• Modeling the dynamic sandwich system with hysteresis using NARMAX model (Q2229807) (← links)
• System identification of Hammerstein models by using backward shift algorithm (Q2246467) (← links)
• Identification of discrete Hammerstein systems by using adaptive finite rational orthogonal basis functions (Q2279609) (← links)
• Modeling and identification of uncertain-input systems (Q2280679) (← links)
• Recursive maximum likelihood method for the identification of Hammerstein ARMAX system (Q2292362) (← links)
• Bayesian semiparametric Wiener system identification (Q2356659) (← links)
• Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems (Q2389435) (← links)
• Computational complexity analysis of set membership identification of Hammerstein and Wiener systems (Q2390559) (← links)
• Filtering-based multistage recursive identification algorithm for an input nonlinear output-error autoregressive system by using the key term separation technique (Q2399050) (← links)
• Hierarchical stochastic gradient algorithm and its performance analysis for a class of bilinear-in-parameter systems (Q2400914) (← links)
• Gradient-based identification methods for Hammerstein nonlinear ARMAX models (Q2432376) (← links)
• A novel APSO-aided maximum likelihood identification method for Hammerstein systems (Q2435639) (← links)
• Newton iterative identification for a class of output nonlinear systems with moving average noises (Q2436948) (← links)
• Towards identification of Wiener systems with the least amount of a priori information on the nonlinearity (Q2440675) (← links)
• Blind maximum likelihood identification of Hammerstein systems (Q2518986) (← links)
• Identification of Hammerstein nonlinear ARMAX systems (Q2576101) (← links)
• Consistent identification of Wiener systems: a machine learning viewpoint (Q2628481) (← links)
• A model-based PID controller for Hammerstein systems using B-spline neural networks (Q2795803) (← links)
• Adaptive control of Hammerstein–Wiener nonlinear systems (Q2822249) (← links)
• New identification method for Hammerstein models based on approximate least absolute deviation (Q2822267) (← links)
• (Q2995235) (← links)
• Identification of Hammerstein systems without explicit parameterisation of non-linearity (Q3632934) (← links)
• Parameter tracking of time-varying Hammerstein-Wiener systems (Q5029102) (← links)
• Identification of Hammerstein–Wiener models with hysteresis front nonlinearities (Q5056575) (← links)
• Adaptive filtering scheme for parameter identification of nonlinear Wiener–Hammerstein systems and its application (Q5130073) (← links)