Interior-Point Method for Nuclear Norm Approximation with Application to System Identification
SIAM Journal on Matrix Analysis and Applications
| Hi-index | 22.14 |
This paper presents a new method for the identification of Hammerstein systems. The parameter estimation problem is formulated as a rank minimization problem by constraining a finite dimensional time dependency between signals. Due to the unknown intermediate signal, the rank minimization problem cannot be solved directly. Thus, the rank minimization problem is reformulated as an intermediate signal construction problem. The main assumption used in this paper is that static nonlinearity is monotonically non-decreasing in order to guarantee a unique combination of a static nonlinear block and a Finite Impulse Response (FIR) linear block. The rank minimization is then relaxed to a convex optimization problem using a nuclear norm. The main contribution of this paper is that the proposed method extends the rank minimization approach to Hammerstein system identification, and does not need a bilinear parametrization and singular value decomposition (SVD), which are commonly used in two-step approaches for Hammerstein system identification.