Worst-Case Identification of Nonlinear Fading Memory Systems

In this paper, the problem of asymptotic identification for a class of nonlinear fading memory systems in the presence of bounded noise is studied. For any experiment, the worst-case error is characterized in terms of the diameter of the worst-case uncertainty set. Optimal inputs that minimize the radius of uncertainty are studied and characterized. Finally, a convergent algorithm that does not require knowledge of the noise upper bound is furnished. The methods as well as the results are quite general and are applicable to a larger variety of settings.