Multi-innovation stochastic gradient algorithms for multi-input multi-output systems

This paper develops a multi-innovation stochastic gradient (MISG) algorithm for multi-input multi-output systems by expanding the innovation vector to an innovation matrix. The convergence analysis shows that the parameter estimates by the MISG algorithm consistently converge to the true parameters under the persistent excitation condition. The MISG algorithm uses not only the current innovation but also the past innovation at each iteration and repeatedly utilizes the available input–output data, thus the parameter estimation accuracy can be improved. The simulation example confirms the theoretical results.