Multi-innovation stochastic gradient algorithm for output error systems based on the auxiliary model

This paper combines the multi-innovation theory with the auxiliary model identification idea to present the auxiliary model based multi-innovation stochastic gradient algorithm by expanding the scalar innovation to an innovation vector and introducing the innovation length. Convergence analysis in the stochastic framework indicates that the parameter estimation error consistently converges to zero under certain excitation condition. Finally, we illustrate and test the proposed algorithm with an example.