A project neural network for solving degenerate quadratic minimax problem with linear constraints

A project neural network for solving degenerate quadratic minimax problem with linear constraints
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用于求解具有线性约束的简并二次极小极大问题的项目神经网络

DOI:
10.1016/j.neucom.2008.05.013
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发表时间:
2009-03
期刊:
影响因子:
6
通讯作者:
--
中科院分区:
计算机科学2区
文献类型:
--
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本文研究了一个带线性约束的二次极大极小问题。混合线性约束和退化是本文所考虑问题的两个重要特征。基于投影性质和李雅普诺夫方法,我们得到了所提出的神经网络的完全收敛性和有限时间收敛性。此外,我们得到了输出轨迹关于Q11和Q22的非奇异部分是指数收敛的。特别地,我们还对退化的无约束二次极大极小问题进行了分析。最后,通过四个算例说明了网络中矩阵H在解决这一问题中的必要性和本文网络的优越性。
In this paper, a quadratic minimax problem with linear constraints is studied. The mixed linear constraints and the degeneracy are the two significant characters of the problem considered in this paper. On the basis of the project properties and Lyapunov method, we get the complete convergence and the finite-time convergence of the proposed neural network in this paper. Moreover, we get that the nonsingular parts of the output trajectories respect to Q11and Q22are exponentially convergent. Particularly, we also give some analysis to the degenerate quadratic minimax problem without constraints. Furthermore, four illustrative examples are given to show the necessity of the matrix H in the network to solve this problem and the superiority of the network in this paper.
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