A state space model is derived for non-uniformly sampled systems. Based on the obtained input/output
representation, a multi-innovation stochastic gradient identification algorithm is presented by expanding the scalar innovation
to an innovation vector. The proposed algorithm uses both the current innovation and the historical innovations, which
improves the stochastic gradient algorithm for the identification accuracy and convergence rate. Simulation example verifies
the superiority of the proposed algorithm by adjusting the innovation length.