Circuits, Systems, and Signal Processing

, Volume 33, Issue 6, pp 1957–1969 | Cite as

Auxiliary Model-Based Forgetting Factor Stochastic Gradient Algorithm for Dual-Rate Nonlinear Systems and its Application to a Nonlinear Analog Circuit

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Abstract

This paper studies the identification problem of dual-rate Hammerstein nonlinear systems. By means of the key-term separation principle, we develop a regression identification model with different input and output sampling rates. In order to promote the convergence rate of the stochastic gradient (SG) algorithm, an auxiliary model-based forgetting factor SG algorithm is derived. Finally, the proposed algorithm is applied to model a nonlinear analog circuit with dual-rate sampling and the simulation result shows the effectiveness of the algorithm.

Keywords

Parameter estimation Recursive identification Hammerstein system Dual-rate sampling Gradient search  Key-term separation principle 
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Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China, the Fundamental Research Funds for the Central Universities (JUDCF11042, JUDCF12031), the PAPD of Jiangsu Higher Education Institutions, and the 111 Project (B12018)

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education)Jiangnan UniversityWuxiPeople’s Republic of China
  2. 2.Jiangsu College of Information TechnologyWuxiPeople’s Republic of China
  3. 3.School of Internet of Things EngineeringJiangnan UniversityWuxiPeople’s Republic of China

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