Identification of Wiener Models with Binary-Valued Output Observations

This work focuses on system identification for Wiener models, whose outputs are measured by binary sensors. It begins with the development of joint identifiability. Then, using periodic inputs, empirical distributions are used to construct identification algorithms. Convergence of the algorithms is established, and associated recursive algorithms are also developed.