W. Greblicki
System identification, Hammerstein system, recursive identification, nonparametric identification, stochastic approximation
This article presents recursive algorithm to recover the nonlinear characteristic of the memoryless part of the Hammerstein system. The a priori information about the system is nonparametric; no functional form of the nonlinearity is known. The algorithm is derived from the idea of stochastic approximation. Its convergence to the characteristic is shown, and the convergence rate is given.
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