Hammerstein systems parameters bounding through sparse polynomial optimization

A single-stage procedure for the evaluation of tight bounds on the parameters of Hammerstein systems from output measurements affected by bounded errors is presented. The identification problem is formulated in terms of polynomial optimization, and relaxation techniques based on linear matrix inequalities are proposed to evaluate parameters bounds by means of convex optimization. The structured sparsity of the identification problem is exploited to reduce the computational complexity of the convex relaxed problem. Convergence proper ties, complexity analysis and advantages of the proposed technique with respect to previously published ones are discussed.