A neurodynamic optimization approach to bilevel linear programming

Sitian Qin, Xinyi Le, Jun Wang

Результат исследований: Вклад в журналСтатья конференциирецензирование

1 Цитирования (Scopus)


This paper presents new results on neurodynamic optimization approach to solve bilevel linear programming problems (BLPPs) with linear inequality constraints. A sub-gradient recurrent neural network is proposed for solving the BLPPs. It is proved that the state convergence time period is finite and can be quantitatively estimated. Compared with existing recurrent neural networks for BLPPs, the proposed neural network does not have any design parameter and can solve the BLPPs in finite time. Some numerical examples are introduced to show the effectiveness of the proposed neural network.

Язык оригиналаАнглийский
Страницы (с-по)418-425
Число страниц8
ЖурналLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Том9377 LNCS
СостояниеОпубликовано - 2015
Опубликовано для внешнего пользованияДа
Событие12th International Symposium on Neural Networks, ISNN 2015 - Jeju, Республика Корея
Продолжительность: 15 окт. 201518 окт. 2015


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