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. © 2013 IEEE.
| Original language | English |
|---|---|
| Article number | 6606815 |
| Pages (from-to) | 824-830 |
| Journal | IEEE Transactions on Neural Networks and Learning Systems |
| Volume | 25 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Apr 2014 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to <a href="mailto:[email protected]">[email protected]</a>.Funding
This work was supported in part by the National Natural Science Foundation of China under Grant 61374078 and in part by the National Priority Research Project under Grant NPRP 4-1162-1-181 funded by Qatar National Research Fund, Qatar.
Research Keywords
- Bilevel linear programming problem (BLPP)
- Differential inclusions
- Nonsmooth analysis
- Recurrent neural network (NN)
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