SEQUENTIAL EXPERIMENTAL DESIGN FOR MISSPECIFIED NONLINEAR MODELS

In design of experiments for nonlinear regression model identifica-tion, the design criterion depends on the unknown parameters to be identified. Classical strategies consist in designing sequentially the experiments by alternating the estimation and design stages. These strategies consider previous observations (collected data) only while estimating the unknown parameters during the estimation stages. This paper proposes to consider the previous observations not only during the estimation stages, but also by the criterion used during the design stages. Furthermore, the proposed criterion considers the robustness requirement: an unknown model error (misspecification) is supposed to exist and is modeled by a kernel-based representa-tion (Gaussian process). Finally, the proposed sequential criterion is compared with a model-robust criterion which does not consider the previously collected data during the design stages, with the classical D-optimal and L-optimal criteria. Index Terms — Sequential design of experiments, Gaussian pro-cess, Nonlinear regression, Robust design, Parameters identification. 1