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A Neural Network Methodology of Quadratic Optimization with Quadratic Equality Constraints

机译:二次平等约束二次优化的神经网络方法

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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:本文介绍了一种反馈经常性神经网络,用于解决基于项目理论和能量函数的二次平等约束(QPQEC)问题的二次编程。在理论方面,我们证明了所提出的神经网络具有一个独特的连续解决方案轨迹,并且当给出初始点时,神经网络的平衡点是稳定的和会聚。使用从[6]的连续近似和收敛定理的想法,可以获得QPQEC问题的最佳解决方案。仿真结果还表明,所提出的反馈经常性神经网络是可行和有效的。

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