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A new method to optimize the satellite broadcasting schedules using the mean field annealing of a Hopfield neural network

机译:一种使用Hopfield神经网络的平均场退火优化卫星广播时间表的新方法

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摘要

Reports a new method for optimizing satellite broadcasting schedules based on the Hopfield neural model in combination with the mean field annealing theory. A clamping technique is used with an associative matrix, thus reducing the dimensions of the solution space. A formula for estimating the critical temperature for the mean field annealing procedure is derived, hence enabling the updating of the mean field theory equations to be more economical. Several factors on the numerical implementation of the mean field equations using a straightforward iteration method that may cause divergence are discussed; methods to avoid this kind of divergence are also proposed. Excellent results are consistently found for problems of various sizes.
机译:报告了一种基于Hopfield神经模型并结合平均场退火理论优化卫星广播时间表的新方法。夹紧技术与关联矩阵一起使用,因此减小了求解空间的尺寸。推导了用于估计平均场退火过程的临界温度的公式,从而使平均场理论方程的更新更加经济。讨论了使用简单迭代方法可能会导致发散的平均场方程数值实现的几个因素;还提出了避免这种差异的方法。对于各种尺寸的问题,始终可以找到出色的结果。

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