Towards a Universal Modeller of Chaotic Systems

被引：1

作者：

Berglund, Erik ^{[1
]}

机构：

[1] Jeppesen, Gothenburg, Sweden

来源：

LEARNING AND INTELLIGENT OPTIMIZATION (LION 11 2017) | 2017年 / 10556卷

关键词：

Machine learning; Self-organization; Chaotic; Recurrence; Recursion; Universal model; NEURAL-NETWORK; ALGORITHM; MAP;

D O I：

10.1007/978-3-319-69404-7_23

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a Machine Learning (ML) algorithm and examines its dynamic properties when trained on chaotic time series. It will be demonstrated that the output of the ML system is itself more chaotic if it is trained on a chaotic input than if it is trained on non-chaotic input. The proposed ML system builds on to the Parameter-Less Self-Organising Map 2 (PLSOM2) and introduces new developments.

引用

页码：307 / 313

页数：7

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