RLS sparse system identification using LAR-based situational awareness

In this paper we propose the combination of the recursive least squares (RLS) and the least angle regression (LAR) algorithms for nonlinear system identification. In the application of interest, the model possesses a large number of coefficients, of which only few are different from zero. We use the LAR algorithm together with a geometrical stopping criterion to establish the number and position of the coefficients to be estimated by the RLS algorithm. The output error is used for indicating model inadequacy and therefore triggering the LAR algorithm. The proposed scheme is capable of modeling intrinsically sparse systems with better accuracy than the RLS algorithm alone, and lower energy consumption.