Switched linear system identification based on bounded-switching clustering

This paper aims at identifying switched linear systems, which are described by noisy input/output data. This problem is originally non-convex and ill-posed. The proposed approach utilizes bounded-switching clustering method to convert the problem into a binary integer optimization and least square. This method optimally divides a time series into several clusters whose parameters are piecewise constant in time. Optimal number and order of linear sub-systems as well as the number of switches are selected using Akaike Information Criterion. The performance of the algorithm is evaluated through simulations. Parameters and structures of switched systems are found accurately in the presence of noise.