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An Artificial Neural Network for Solving Distributed Optimal Control of the Poisson's Equation

机译:求解泊松方程分布式最优控制的人工神经网络

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摘要

This paper presents a simple and efficient method based on artificial neural network to solve distributed optimal control of Poisson's equation with Dirichlet boundary condition. The trial solutions are used to approximate the state and control variables. These trial solutions are considered by using a single layer neural network. By replacing the trial solutions in objective function and Poisson's equation, then using the weighted residual method, distributed optimal control of Poisson's equation is converted to a linear quadratic optimal control problem. The weights of the trial solutions are computed by solving the new problem. In order to solve the linear quadratic optimal control problem, the Pontryagin maximum principle is used. Finally we apply the proposed method on several examples that in computational experiments, the high efficiency of the presented method is illustrated.
机译:本文提出了一种基于人工神经网络的简单有效的方法来求解具有Dirichlet边界条件的Poisson方程的分布式最优控制。试用解决方案用于近似状态和控制变量。通过使用单层神经网络来考虑这些试验解决方案。通过替换目标函数和泊松方程的试验解,然后使用加权残差法,将泊松方程的分布式最优控制转换为线性二次最优控制问题。通过解决新问题来计算试验解决方案的权重。为了解决线性二次最优控制问题,使用庞特里亚金极大值原理。最后,我们将所提出的方法应用于几个实例,这些实例在计算实验中证明了该方法的高效率。

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