A new kernel-based approach for system identification

We propose a new-kernel based approach for linear system identification. The impulse response is modeled as realization of a Gaussian process which includes information on smoothness and BIBO-stability. The corresponding minimum- variance estimate belongs to a Reproducing kernel Hilbert space which is given a spectral characterization and shown to be dense in the space of continuous functions. The approach may prove particularly useful in order to obtain reduced order models and assess the corresponding bias error in the context of robust identification. Several benchmarks taken from the literature demonstrate the effectiveness of the proposed approach.