Performance analysis of the auxiliary models based multi-innovation stochastic gradient estimation algorithm for output error systems ?
This paper combines the multi-innovation identification theory and the auxiliary model identification idea and presents an 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 estimates given by the proposed algorithm can fast converge to their true values. Finally, we illustrate and test the proposed algorithm with an example.
stochastic gradient multi-innovation identification auxiliary model identification idea parameter estimation recursive identification least squares convergence properties martingale convergence theorem