Convergence of least squares estimators in the adaptive Wynn algorithm for some classes of nonlinear regression models

The paper continues the authors' work on adaptive Wynn algorithm in a nonlinear regression model. In present it is shown that if mean response function satisfies condition of `saturated identifiability', which was introduced by Pronzato \cite{Pronzato}, then least squares estimators are strongly consistent. states parameter identifiable under any saturated design, i.e., values at $p$ distinct design points determine point uniquely where, typically, dimension vector. Further essential assumptions compactness experimental region and space together with some natural continuity assumptions. If true an interior smoothness asymptotic homoscedasticity random errors normality obtained.