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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\u2010making performance via parameter optimization. We first evaluate the negative autocorrelation in a chaotic time series and its impact on decision\u2010making detail. Then, we analyze the decision\u2010making ability using three different surrogate chaos time series to examine the underlying mechanism. We clarify that the negative autocorrelation of laser chaos improves decision\u2010making 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\u2010making performance. This study provides a new insight into exploiting the supremacy of chaotic dynamics in artificially constructed intelligent systems.<\/jats:p>","DOI":"10.1155\/2021\/8877660","type":"journal-article","created":{"date-parts":[[2021,2,26]],"date-time":"2021-02-26T18:06:36Z","timestamp":1614362796000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Analysis on Effectiveness of Surrogate Data\u2010Based Laser Chaos Decision Maker"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9318-1036","authenticated-orcid":false,"given":"Norihiro","family":"Okada","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5638-8022","authenticated-orcid":false,"given":"Mikio","family":"Hasegawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Chauvet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0618-4894","authenticated-orcid":false,"given":"Aohan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8982-9824","authenticated-orcid":false,"given":"Makoto","family":"Naruse","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,2,26]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms1476"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1364\/oe.20.003241"},{"key":"e_1_2_10_3_2","article-title":"Neuromorphic photonic networks using silicon photonic 356 weight banks","volume":"7","author":"Tait A. 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