Previous studies have developed pseudo-random number generators, where a pseudo-random number is not perfectly random but is practically useful. In this paper, we propose a new system for pseudo-random number generation. Recurrent neural networks with long short-term memory units are used to mimic the appearance of a given sequence of irrational number (e.g., pi), and these are intended to generate pseudo-random numbers in an iterative manner. We design algorithms to ensure that the output sequence contains no repetition or pattern. Through experimental results, we can observe the potential of the proposed system in terms of its randomness and stability. As this system can be used for parameter approximation in machine learning techniques, we believe that it will contribute to various industrial fields such as traffic management and frameworks for sensor networks.

中文翻译：

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使用 LSTM 生成伪随机数

以前的研究开发了伪随机数生成器，其中伪随机数不是完全随机的，但实际上很有用。在本文中，我们提出了一种新的伪随机数生成系统。具有长短期记忆单元的循环神经网络用于模拟给定无理数序列（例如 pi）的出现，并且这些网络旨在以迭代方式生成伪随机数。我们设计算法以确保输出序列不包含重复或模式。通过实验结果，我们可以观察所提出的系统在随机性和稳定性方面的潜力。由于该系统可用于机器学习技术中的参数逼近，