神经网络的“熵产率”和“熵”

Hung-Jen Chang, Kung-Shiuh Huang, Kuan-Tsao Huang
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引用次数: 1

摘要

导出了神经网络的两个新量——熵产率和熵。在Hopfield神经模型中,Hopfield引入了一个量,能量,能量最小值对应一个问题可能的好的解决方案。结果表明,能量函数与物理学中能量的物理意义不相符;一个更好的物理解释可以通过熵产率和物理学中的熵。这些新量可以进一步推广到神经网络的一般非平衡开放系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
'Entropy production rate' and 'entropy' for neural networks
Two new quantities for neural networks, entropy production rate and entropy, are derived. In the Hopfield neural model, Hopfield introduced a quantity, energy, and the energy minimum corresponds to a possible good solution to a problem. It is shown that the energy function does not match the physical meaning of energy in physics; a better physical interpretation can go through entropy production rate and entropy in physics. These new quantities can be further extended to general nonequilibrium open systems for neural networks.<>
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