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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...
This paper develops a multi-innovation stochastic gradient (MISG) algorithm for multi-input, multi-output systems. The convergence analysis using the martingale convergence theorem 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...
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