One-layer Neural Network for Solving Least Absolute Deviation Problems with Box and Equality Constraints
Cuiping Li
Journal of Electronic Systems, Vol. 8, No. 2, 2018.
DOI: 10.6025/jes/2018/8/2/57-71
Link: http://www.dline.info/jes/fulltext/v8n2/jesv8n2_2.pdf
Abstract:
In this paper, I design a new neural network for solving least absolute deviation problems with equality and box
constraints. Compared with some existing models, the proposed neural network needs the fewest state variables and has only
one-layer structure. By constructed a proper Lyapunov function, the following two results can be proved. First, it is stable in
the sense of Lyapunov. Second, the solution of the proposed model can converge to an exact optimal solution of the problem.
Finally, its validity and transient behaviors are demonstrated by some simulation results.