Multi-innovation extended stochastic gradient algorithm and its performance analysis. (English) Zbl 1196.94026

Summary: This paper derives the multi-innovation extended stochastic gradient algorithm for controlled autoregressive moving average models by expanding the scalar innovation to an innovation vector and analyzes its performance in detail. Four convergence theorems are given for the multi-innovation extended stochastic gradient algorithm to show that the parameter estimates converge to their true values under the weak persistent excitation condition. The simulation results show that the proposed algorithm can produce more accurate parameter estimates than the traditional extended stochastic gradient algorithm.


94A12 Signal theory (characterization, reconstruction, filtering, etc.)
93E10 Estimation and detection in stochastic control theory
94-04 Software, source code, etc. for problems pertaining to information and communication theory
93-04 Software, source code, etc. for problems pertaining to systems and control theory
Full Text: DOI


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