Analysis on Effectiveness of Surrogate Data-Based Laser Chaos Decision Maker
Analysis on Effectiveness of Surrogate Data-Based Laser Chaos Decision Maker
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DOI：
10.1155/2021/8877660
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发表时间：
2021-02-26
期刊：
影响因子：
2.3
通讯作者：
Naruse, Makoto
中科院分区：
文献类型：
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作者：
Okada, Norihiro;Hasegawa, Mikio;Naruse, Makoto
激光混乱决策器已被证明能够实现多臂强盗问题的超高速解决方案或GHz量级的决策。然而，其潜在机制尚未完全了解。在本文中，我们分析了实验观察到的激光混沌时间序列中固有的混沌动力学通过替代数据，并通过参数优化进一步加快决策性能。我们首先评估了混沌时间序列中的负自相关性及其对决策细节的影响。然后，我们分析了决策能力，使用三个不同的代理混沌时间序列来检查的潜在机制。我们澄清了激光混沌的负自相关性改善了决策，并且原始激光混沌时间序列的幅度分布不是最优的。因此，我们引入了一个新的参数来调整激光混沌的振幅分布，以提高决策性能。这项研究提供了一个新的洞察力，利用至高无上的混沌动力学在人工构建的智能系统。
The laser chaos decision maker has been demonstrated to enable ultra-high-speed solutions of multiarmed bandit problems or decision-making in the GHz order. However, the underlying mechanisms are not well understood. In this paper, we analyze the chaotic dynamics inherent in experimentally observed laser chaos time series via surrogate data and further accelerate the decision-making performance via parameter optimization. We first evaluate the negative autocorrelation in a chaotic time series and its impact on decision-making detail. Then, we analyze the decision-making ability using three different surrogate chaos time series to examine the underlying mechanism. We clarify that the negative autocorrelation of laser chaos improves decision-making and that the amplitude distribution of the original laser chaos time series is not optimal. Hence, we introduce a new parameter for adjusting the amplitude distribution of the laser chaos to enhance the decision-making performance. This study provides a new insight into exploiting the supremacy of chaotic dynamics in artificially constructed intelligent systems.