Polynomial extension of linear subspace algorithms for stochastic identification

Among the algorithms of linear models identification from input/output data, the N4SID (numerical sub-space state space system identification) plays an important role due to its simplicity and effectiveness. It is known that N4SDD gives good results for system identification in a Gaussian setting. This paper presents a technique that improves the performances of the N4SID in the case of a nonGaussian data set. The approach here followed is in the framework of polynomial estimation theory, developed in recent years, which is a simple and effective tool for the processing of nonGaussian data.