IJCAS

The iterative parameter estimation methods for a class of nonlinear systems with interval-varying measurements are studied in this paper. According to the auxiliary model identification idea, an auxiliary model is constructed to estimate the unknown noise-free process outputs, and an interval-varying auxiliary model gradient-based iterative identification algorithm is developed. Furthermore, a particle filter, which uses some discrete random sampling points to approximate the posterior probability density function, is adopted to compute the output estimates. Then an interval-varying particle filtering gradient-based iterative algorithm is derived, and an interval-varying auxiliary model based stochastic gradient (V-AM-SG) algorithm is presented for comparison. The simulation results indicate that the proposed algorithms are effective for identifying the nonlinear systems with interval-varying measurements, and can generate more accurate parameter estimates than the V-AM-SG algorithm.

Keywords: Auxiliary model, bilinear system, interval-varying measurements, parameter estimation, particle filtering