A Class of Diffusion Zero Attracting Stochastic Gradient Algorithms With Exponentiated Error Cost Functions

In this paper, a class of diffusion zero-attracting stochastic gradient algorithms with exponentiated error cost functions is put forward due to its good performance for sparse system identification. Distributed estimation algorithms based on the popular mean-square error criterion have poor behavio...

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Veröffentlicht in:IEEE access Jg. 8; S. 4885 - 4894
Hauptverfasser: Luo, Zhengyan, Zhao, Haiquan, Zeng, Xiangping
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptation models
Adaptive systems
Algorithms
Computer networks
Cost function
Diffusion
Diffusion adaptive algorithms
Errors
Estimation
exponentiated error
Performance degradation
Polynomials
Scaling factors
Signal processing algorithms
sparse system
Steady state
Stochastic processes
System identification
variable scaling factor
zero attracting
ISSN:2169-3536, 2169-3536
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Zusammenfassung:In this paper, a class of diffusion zero-attracting stochastic gradient algorithms with exponentiated error cost functions is put forward due to its good performance for sparse system identification. Distributed estimation algorithms based on the popular mean-square error criterion have poor behavior for sparse system identification with color noise. To overcome this drawback, a class of stochastic gradient least exponentiated (LE) algorithms with exponentiated error cost functions were proposed, which achieved a low steady-state compared with the least mean square (LMS) algorithm. However, those LE algorithms may suffer from performance deterioration in the spare system. For sparse system identification in the adaptive network, a polynomial variable scaling factor improved diffusion least sum of exponentials (PZA-VSIDLSE) algorithm and an l p -norm constraint diffusion least exponentiated square (LP-DLE2) algorithm are proposed in this work. Instead of using the l 1 -norm penalty, an l p -norm penalty and a polynomial zero-attractor are employed as a substitution in the cost functions of the LE algorithms. Then, we perform mean behavior model and mean square behavior modal of the LP-DLE2 algorithm with several common assumptions. Moreover, simulations in the context of distributed network sparse system identification show that the proposed algorithms have a low steady-state compared with the existing algorithms.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2961162