- Title
- A recurrent neural network for solving bilevel linear programming problem
- Creator
- He, Xing; Li, Chuandong; Huang, Tingwen; Li, Chaojie; Huang, Junjian
- Date
- 2014
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/40505
- Identifier
- vital:5866
- Identifier
- ISSN:2162-2388
- 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.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Transactions on Neural Networks and Learning Systems Vol. 25, no. 4 (April 2014 2014), p. 824-830
- Rights
- © 2013 IEEE.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Bilevel linear programming problem (BLPP); Differential inclusions; Nonsmooth analysis; Recurrent neural network (NN); Differential equations; Linear programming; Supply chains; Bilevel linear programming; Chain distribution; Equilibrium point; Non-smooth analysis; Optimal solutions; Penalty function; Simple structures; Recurrent neural networks
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