Identification of switched nonlinear ARX systems using a randomized algorithm

The identification of switched nonlinear systems is a mixed discrete and continuous optimization problem that involves complex combinatorial problems, such as the selection of the local model structures and the estimation of the switching signal. In particular, switchings can occur at any time, which makes the combinatorial complexity of the latter task increase exponentially with the number of data. In this work, we employ a randomized scheme to estimate the switching locations: a probability distribution is used to represent the locations of a finite number of switchings and a sample-andevaluate strategy is employed to tune it. This strategy smoothly integrates with a previously developed randomized method devoted to the identification of the nonlinear local models and the mode switching sequence to produce a full switched system identification method. The proposed solution has been tested on a benchmark example to verify its effectiveness and efficiency compared to existing works.