Multi-innovation Extended Stochastic Gradient Algorithm and Its Performance Analysis

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...

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Veröffentlicht in:Circuits, systems, and signal processing Jg. 29; H. 4; S. 649 - 667
Hauptverfasser: Liu, Yanjun, Yu, Li, Ding, Feng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston SP Birkhäuser Verlag Boston 01.08.2010
Springer Nature B.V
Schlagworte:
Algorithms
Circuits and Systems
Convergence
Electrical Engineering
Electronics and Microelectronics
Engineering
Estimates
Instrumentation
Mathematical analysis
Mathematical models
Parameter estimation
Scalars
Signal,Image and Speech Processing
Stochastic models
Stochasticity
Theorems
Vector space
Vectors (mathematics)
Signal processing
Recursive identification
Parameter estimation
Performance analysis
Multi-innovation identification
Stochastic gradient
ISSN:0278-081X, 1531-5878
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Zusammenfassung: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.
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ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-010-9174-8