A recurrent neural network for solving bilevel linear programming problem

He, X, Li, C, Huang, T, Li, C and Huang, J 2014, 'A recurrent neural network for solving bilevel linear programming problem', IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 4, pp. 824-830.


Document type: Journal Article
Collection: Journal Articles

Title A recurrent neural network for solving bilevel linear programming problem
Author(s) He, X
Li, C
Huang, T
Li, C
Huang, J
Year 2014
Journal name IEEE Transactions on Neural Networks and Learning Systems
Volume number 25
Issue number 4
Start page 824
End page 830
Total pages 7
Publisher IEEE
Abstract In this brief, based on the method of penalty functions, a recurrent neural network (NN) modeled by means of a differential inclusion is proposed for solving the bilevel linear programming problem (BLPP). Compared with the existing NNs for BLPP, the model has the least number of state variables and simple structure. Using nonsmooth analysis, the theory of differential inclusions, and Lyapunov-like method, the equilibrium point sequence of the proposed NNs can approximately converge to an optimal solution of BLPP under certain conditions. Finally, the numerical simulations of a supply chain distribution model have shown excellent performance of the proposed recurrent NNs.
Subject Neurocognitive Patterns and Neural Networks
Keyword(s) Bilevel linear programming problem (BLPP)
Differential inclusions
Nonsmooth analysis
Recurrent neural network (NN)
DOI - identifier 10.1109/TNNLS.2013.2280905
Copyright notice © 2013 IEEE
ISSN 2162-237X
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