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A neural network approach for solving nonlinear bilevel programming problem

机译:解决非线性双层规划问题的神经网络方法

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摘要

A neural network model is presented for solving nonlinear bilevel programming problem, which is a NP-hard problem. The proposed neural network is proved to be Lyapunov stable and capable of generating approximal optimal solution to the nonlinear bilevel programming problem. The asymptotic properties of the neural network are analyzed and the condition for asymptotic stability, solution feasibility and solution optimality are derived. The transient behavior of the neural network is simulated and the validity of the network is verified with numerical examples.
机译:提出了用于解决非线性双层规划问题的神经网络模型,该问题是一个NP难问题。所提出的神经网络被证明是Lyapunov稳定的,并且能够为非线性双层规划问题生成近似最优解。分析了神经网络的渐近性质,推导了渐近稳定性的条件,解的可行性和解的最优性。仿真了神经网络的瞬态行为,并通过数值例子验证了网络的有效性。

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