Recursive Geman-McClure Estimator for Implementing Second-Order Volterra Filter

The second-order Volterra (SOV) filter is a powerful tool for modeling the nonlinear system. The Geman-McClure estimator, whose loss function is non-convex and has been proven to be a robust and efficient optimization criterion for learning system. In this brief, we present a SOV filter, named SOV r...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. II, Express briefs Express briefs, 2019-07, Vol.66 (7), p.1272-1276
Main Authors: Lu, Lu, Wang, Wenyuan, Yang, Xiaomin, Wu, Wei, Zhu, Guangya
Format: Article
Language:English
Subjects:
Adaptive algorithm
Adaptive algorithms
Adaptive filters
Circuit stability
Circuits and systems
Computer simulation
Error analysis
Geman-McClure estimator
Nonlinear systems
Optimization
Probability density function
recursive version
Stability analysis
Stability criteria
Steady-state
α-stable noise
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Summary:The second-order Volterra (SOV) filter is a powerful tool for modeling the nonlinear system. The Geman-McClure estimator, whose loss function is non-convex and has been proven to be a robust and efficient optimization criterion for learning system. In this brief, we present a SOV filter, named SOV recursive Geman-McClure, which is an adaptive recursive Volterra algorithm based on the Geman-McClure estimator. The mean stability and mean-square stability (steady-state excess mean square error) of the proposed algorithm is analyzed in detail. Simulation results support the analytical findings and show the improved performance of the proposed new SOV filter as compared with existing algorithms in both Gaussian and impulsive noise environments.
ISSN:1549-7747
1558-3791
DOI:10.1109/TCSII.2018.2875039