Abstract
This paper proposes a simplified neural network for generalized least absolute deviation by transforming its optimization conditions into a system of double projection equations. The proposed network is proved to be stable in the sense of Lyapunov and converges to an exact optimization solution of the original problem for any starting point. Compared with the existing neural networks for generalized least absolute deviation, the new model has the least neurons and low complexity and is suitable to parallel implementation. The validity and transient behavior of the proposed neural network are demonstrated by numerical examples.
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Acknowledgements
The authors would like to thank the Editor-in-Chief and three anonymous reviewers for their insightful and constructive comments, which have enriched the content and improved the presentation of this paper. This work was supported in part by the National Natural Science Foundation of China under Grant 61273311, and the Fundamental Research Funds for the Central Universities under Grant GK201603002.
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Li, Y., Gao, X. Simplified neural network for generalized least absolute deviation. Neural Comput & Applic 29, 1455–1463 (2018). https://doi.org/10.1007/s00521-017-3060-2
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DOI: https://doi.org/10.1007/s00521-017-3060-2