On joint optimal experiment design for identifying partition and local model parameters of Takagi-Sugeno models
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Proceedings of the 17th IFAC Symposium on System Identification (SysID), Seite 1427 -- 1432. Beijing, China, (Oktober 2015)

Optimal Experiment Design (OED) is a well-developed concept for regression problems that are linear-in-their-parameters or for linear dynamical models. In case of nonlinear Takagi-Sugeno models either non-model-based experiment design or OED restricted to the local model parameters has been examined. This article proposes a joint design of local model and partition parameters that bases on the Fisher Information Matrix (FIM). For this purpose, a symbolic description of the joint FIM is derived. Its heterogeneous structure can make it badly conditioned, complicating computation of the determinant for D-optimal design. This problem is relaxed using determinant decomposition. A theoretical analysis and a case study show that experiment design for local model and partition parameters may significantly differ from each other.
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