Wlodzimierz Greblicki

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Derived from the idea of stochastic approximation, recursive algorithms to identify a Hammerstein system are presented. Two of them recover the characteristic of the nonlinear memoryless subsystem while the third one estimates the impulse response of the linear dynamic part. The a priori information about both subsystems is nonparametric. Consistency in(More)
—The characteristic of the nonlinear part of the Wiener system is estimated. The system is driven by a Gaussian random process which may not be white. Three algorithms are presented, two semirecursive and one of the off-line type. Their pointwise convergence in probability is shown and results of numerical simulation are given. Index Terms—System identi(More)
The nonlinear characteristic of a Wiener system is estimated under nonparametric <i>a priori</i> information. The probability density of the input signal is completely unknown and can be of any form. The estimate is asymptotically biased but its bias can be made arbitrarily small. Results of computer simulations are presented.