IEEE Trans Neural Netw Learn Syst. 2015 Nov;26(11):2891-900. doi: 10.1109/TNNLS.2015.2425301. Epub 2015 May 7.
This paper presents a projection neural network described by a dynamic system for solving constrained quadratic minimax programming problems. Sufficient conditions based on a linear matrix inequality are provided for global convergence of the proposed neural network. Compared with some of the existing neural networks for quadratic minimax optimization, the proposed neural network in this paper is capable of solving more general constrained quadratic minimax optimization problems, and the designed neural network does not include any parameter. Moreover, the neural network has lower model complexities, the number of state variables of which is equal to that of the dimension of the optimization problems. The simulation results on numerical examples are discussed to demonstrate the effectiveness and characteristics of the proposed neural network.
本文提出了一种投影神经网络，由一个动态系统描述，用于解决约束二次极大极小规划问题。为所提出的神经网络的全局收敛性提供了基于线性矩阵不等式的充分条件。与二次极大极小优化的一些现有神经网络相比，本文提出的神经网络能够解决更一般的约束二次极大极小优化问题，并且所设计的神经网络不包含任何参数。此外，该神经网络具有更低的模型复杂度，其状态变量的数量等于优化问题的维度。通过数值示例讨论了仿真结果，以验证所提出的神经网络的有效性和特点。
