Hierarchical Stochastic Gradient Algorithm and its Performance Analysis for a Class of Bilinear-in-Parameter Systems

This paper considers the parameter identification for a special class of nonlinear systems, i.e., bilinear-in-parameter systems. Based on the hierarchical identification principle, a hierarchical stochastic gradient (HSG) estimation algorithm is presented. The basic idea is to decompose a bilinear-i...

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Vydáno v:Circuits, systems, and signal processing Ročník 36; číslo 4; s. 1393 - 1405
Hlavní autoři: Ding, Feng, Wang, Xuehai
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.04.2017
Springer Nature B.V
Témata:
Algorithms
Circuits and Systems
Computer simulation
Dynamical systems
Electrical Engineering
Electronics and Microelectronics
Engineering
Estimating
Instrumentation
Nonlinear dynamics
Nonlinear programming
Parameter estimation
Parameter identification
Performance evaluation
Signal,Image and Speech Processing
Simulation
Stochasticity
Gradient search
Bilinear-in-parameter system
Parameter estimation
Performance analysis
Hierarchical identification
ISSN:0278-081X, 1531-5878
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Shrnutí:This paper considers the parameter identification for a special class of nonlinear systems, i.e., bilinear-in-parameter systems. Based on the hierarchical identification principle, a hierarchical stochastic gradient (HSG) estimation algorithm is presented. The basic idea is to decompose a bilinear-in-parameter system into two subsystems and to derive the HSG identification algorithm for estimating the system parameters by replacing the unknown variables in the information vectors with their estimates obtained at the previous time. The convergence analysis of the proposed algorithm indicates that the parameter estimation errors converge to zero under persistent excitation conditions. The simulation results show that the proposed algorithm is effective.
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ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-016-0367-7