Non-Convex Projection Adaptive Hammerstein Filtering

LIU Zhaoting; BAO Huiming; YAO Yingbiao

doi:10.11999/JEIT220171

Volume 45 Issue 4

Apr. 2023

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LIU Zhaoting, BAO Huiming, YAO Yingbiao. Non-Convex Projection Adaptive Hammerstein Filtering[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1313-1320. doi: 10.11999/JEIT220171

Citation:

LIU Zhaoting, BAO Huiming, YAO Yingbiao. Non-Convex Projection Adaptive Hammerstein Filtering[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1313-1320. doi: 10.11999/JEIT220171

LIU Zhaoting, BAO Huiming, YAO Yingbiao. Non-Convex Projection Adaptive Hammerstein Filtering[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1313-1320. doi: 10.11999/JEIT220171

Citation:

LIU Zhaoting, BAO Huiming, YAO Yingbiao. Non-Convex Projection Adaptive Hammerstein Filtering[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1313-1320. doi: 10.11999/JEIT220171

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Non-Convex Projection Adaptive Hammerstein Filtering

doi: 10.11999/JEIT220171

School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China

Funds: The National Natural Science Foundation of China(61677192), The Opening Foundation of Zhejiang Engineering Research Center of MEMS(MEMSZJERC2204)

Received Date: 2022-02-22
Rev Recd Date: 2022-04-28

Available Online: 2022-06-28

Publish Date: 2023-04-10

Abstract

Abstract

This paper focuses on adaptive filtering techniques for parameter identification of Hammerstein systems and output prediction of nonlinear systems. By formulating the underlying filtering problem as a recursive bilinear least-squares optimization with the non-convex feasible region constraint, an algorithmic framework is developed based on recursive least-squares and Alternating Direction Method of Multipliers (ADMM). Under this framework, the solution to nonconvex constraint optimization problem can be obtained by implementing ridge regression and Euclidean projection. Simulation results in the context of system identification, nonlinear predication, and acoustic echo cancellation, reveal that the proposed algorithm has good convergence and stability, and can obtain more accurate identification and prediction results.
- System identification,
- Nonlinear prediction,
- Adaptive filtering,
- Parameter estimation,
- Hammerstein system

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References(17)

References

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