A Generalized Stochastic Approximation for the Recursive System Identification

This paper presents an approach for constructing probabilistic approximation type algorithms used in system identification systems. The proposed approach allows obtaining recursive identification algorithms under fairly mild assumptions about noise and disturbances that distort the system. The obtained algorithm does not require inversion of the Hessian of the identification criterion and is robust to changes in the order of the Hessian. This example demonstrates good convergence properties of the obtained algorithm compared to conventional recursive systems. Copyright (c) 2023 The Authors.