Auxiliary Model-Based Forgetting Factor Stochastic Gradient Algorithm for Dual-Rate Nonlinear Systems and its Application to a Nonlinear Analog Circuit

This paper studies the identification problem of dual-rate Hammerstein nonlinear systems. By means of the key-term separation principle, we develop a regression identification model with different input and output sampling rates. In order to promote the convergence rate of the stochastic gradient (SG) algorithm, an auxiliary model-based forgetting factor SG algorithm is derived. Finally, the proposed algorithm is applied to model a nonlinear analog circuit with dual-rate sampling and the simulation result shows the effectiveness of the algorithm.

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