AbstractRecursive identification algorithms based on the Wiener model are presented in this paper. They estimate the parameters of a SISO linear dynamic block in cascade with a known static output non‐linearity. Inversion of the non‐linear function is avoided and approximations of gradients are utilized. This allows an exact treatment of output measurement saturation and of situations where output measurements are obtained from sensors with relay‐type characteristics, such as EGO sensors in emission control systems for cars. Exact compensation for coarse quantization of output measurements can also be obtained by the algorithms. Stochastic averaging techniques using associated differential equations prove that local and global convergence of the schemes are tied to positive realness and sector conditions on the non‐linearity. Conditions for local convergence to the correct parameters are established for the case where the output non‐linearity is an arbitrary
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