首页> 外文会议>International Symposium on Neural Networks(ISNN 2005) pt.1; 20050530-0601; Chongqing(CN) >A Neural Network Methodology of Quadratic Optimization with Quadratic Equality Constraints
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A Neural Network Methodology of Quadratic Optimization with Quadratic Equality Constraints

机译:具有二次等式约束的二次优化的神经网络方法

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摘要

This paper presents a feedback recurrent neural network for solving the quadratic programming with quadratic equality constraint (QPQEC) problems based on project theory and energy function. In the theoretical aspect, we prove that the proposed neural network has one unique continuous solution trajectory and the equilibrium point of neural network is stable and convergent when the initial point is given. Employing the idea of successive approximation and convergence theorem from [6], the optimal solution of QPQEC problem can be obtained. The simulation result also shows that the proposed feedback recurrent neural network is feasible and efficient.
机译:本文提出了一种基于项目理论和能量函数的求解带有二次等式约束(QPQEC)问题的二次规划的反馈递归神经网络。从理论上讲,我们证明了所提出的神经网络具有唯一的连续解轨迹,并且在给出初始点时神经网络的平衡点是稳定且收敛的。利用[6]的逐次逼近和收敛定理,可以得到QPQEC问题的最优解。仿真结果还表明,所提出的反馈递归神经网络是可行和有效的。

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