A Recurrent Neural Network for Solving Bilevel Linear Programming Problem

A Recurrent Neural Network for Solving Bilevel Linear Programming Problem
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解决二层线性规划问题的循环神经网络

DOI:
10.1109/tnnls.2013.2280905
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发表时间:
2014-04
影响因子:
10.4
通讯作者:
Huang Junjian
Huang Junjian
中科院分区:
计算机科学1区
文献类型:
--
作者:
He Xing;Li Chu;ong;Huang Tingwen;Li Chaojie;Huang Junjian

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在本文中,基于罚函数法,提出了一种通过微分包含建模的递归神经网络(NN)来解决双层线性规划问题(BLPP)。与现有的用于双层线性规划问题的神经网络相比,该模型
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.
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