Least squares and stochastic gradient parameter estimation for multivariable nonlinear Box‐Jenkins models based on the auxiliary model and the multi‐innovation identification theory-Reference-Cited by-同舟云学术

Least squares and stochastic gradient parameter estimation for multivariable nonlinear Box‐Jenkins models based on the auxiliary model and the multi‐innovation identification theory

Published:2012-11-09 Issue:8 Volume:29 Page:907-921
ISSN:0264-4401
Container-title:Engineering Computations
language:en
Short-container-title:Engineering Computations

Author:

Chen Jing,Ding Feng

Publisher

Emerald

Subject

Computational Theory and Mathematics,Computer Science Applications,General Engineering,Software

Reference56 articles.

1. Modified stochastic gradient identification algorithms with fast convergence rates

2. Gradient based estimation algorithm for Hammerstein systems with saturation and dead-zone nonlinearities

3. Auxiliary model based multi-innovation algorithms for multivariable nonlinear systems

4. Several multi-innovation identification methods

5. Combined parameter and output estimation of dual-rate systems using an auxiliary model

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