Hayato Nakada

Identification of piecewise affine systems based on statistical clustering technique

Abstract This paper is concerned with the identification of a class of piecewise affine systems called a piecewise affine autoregressive exogenous model by using a statistical clustering algorithm based on a Gaussian mixture model. It is shown that the present statistical clustering approach enables us to estimate the number of sub-models based on the information criteria such as CAIC and MDL.

Reachable Set Analysis of Uncertain Piecewise Affine Systems and its Application to T-S Fuzzy Systems

Abstract This paper is concerned with analysis of the reachable sets of piecewise affine systems with polytopic uncertainties and energy-bounded disturbances. We derive a linear matrix inequality condition which explicitly provides an outer approximation of the reachable set in terms of a piecewise quadratic form. Moreover, we apply the analysis result to analysis of Takagi-Sugeno fuzzy systems.