A feedback neural network for solving convex constraint optimization problems
A feedback neural network for solving convex constraint optimization problems
复制标题
DOI：
10.1016/j.amc.2007.12.029
复制
发表时间：
2008-07
期刊：
影响因子：
--
通讯作者：
Yongqing Yang;Jinde Cao
中科院分区：
文献类型：
--
作者：
Yongqing Yang;Jinde Cao
本文分两步提出了一种反馈神经网络模型。首先，通过引入能量函数建立了一个带约束的凸次优化问题，并基于投影方法构造了求解该次优化问题的神经子网络。其次，利用该子网络构造了一个反馈神经网络，该网络能够收敛到原优化问题的精确最优解。所提出的反馈网络的显着特点是没有拉格朗日乘子，没有对偶变量，没有惩罚参数。它具有状态变量最少、结构简单、易于硬件实现等优点。最后通过两个仿真算例验证了该方法的可行性和有效性。
In this paper, a feedback neural network model is presented by two steps. Firstly, a convex sub-optimization problem with bound constraints is established by introducing an energy function and the neural subnetwork for solving the sub-optimization problem is constructed based on the projection method. Secondly, a feedback neural network is proposed by using the subnetwork and can converge to an exact optimal solution of primal optimization problem. The distinguishing features of the proposed feedback network are no Lagrange multipliers, no dual variables, and no penalty parameters. It has the least number of state variables, simple structure, and is suitable for hardware implementation. Two simulation examples are provided to show the feasibility and efficiency of proposed method in the paper.