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Abstract This paper presents a neural network for solving least absolute deviation problems with equality and box constraints. Compared with some existing models, the proposed neural network has fewer state… Click to show full abstract
Abstract This paper presents a neural network for solving least absolute deviation problems with equality and box constraints. Compared with some existing models, the proposed neural network has fewer state variables and only one-layer structure. The proposed model is proved to be Lyapunov stable and converge to an exact optimal solution of the original problem. Some simulation results show the validity and transient behavior of the proposed neural network.
Keywords:
absolute deviation;
network;
solving least;
network solving;
least absolute;
neural network
Journal Title: Neurocomputing
Year Published: 2019
Link to full text (if available)
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Journal Title: Neurocomputing
Year Published: 2019
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!