Multi-innovation stochastic gradient learning identification algorithm

This paper presents 2-degrees-of-freedom multi-innovation learning identification methods for a class of stochastic systems with time-varying parametric uncertainties. The systems undertaken perform tasks repetitively over a pre-specified finite-time interval, and several multi-innovation stochastic gradient learning identification algorithms are derived on the basis of the repetitive operations. Numerical results are presented to demonstrate the effectiveness of the proposed multi-innovation stochastic gradient learning identification algorithms.