A multi-level and multi-scale evolutionary modeling system for scientific data

Zhou Kang, Yan Li, H. de Garis, Lishan Kang
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引用次数: 0

Abstract

The discovery of scientific laws is always built on the basis of scientific experiments and observed data. Any real world complex system must be controlled by some basic laws, including macroscopic level, submicroscopic level and microscopic level laws. How to discover its necessity-laws from these observed data is the most important task of data mining (DM) and KDD. Based on the evolutionary computation, this paper proposes a multilevel and multi-scale evolutionary modeling system which models the macro-behavior of the system by ordinary differential equations while models the micro-behavior of the system by natural fractals. This system can be used to model and predict the scientific observed time series, such as observed data of sunspot and precipitation of flood season, and always get good results.
多层次、多尺度的科学数据演化建模系统
科学规律的发现总是建立在科学实验和观察数据的基础上。任何现实世界的复杂系统都必须遵循一些基本规律,包括宏观规律、亚微观规律和微观规律。如何从这些观测数据中发现其必然规律是数据挖掘和知识发现的重要任务。在进化计算的基础上,提出了一个多层次、多尺度的进化建模系统,用常微分方程对系统的宏观行为进行建模,用自然分形对系统的微观行为进行建模。该系统可用于对太阳黑子观测资料、汛期降水等科学观测时间序列进行建模和预测,并取得了较好的结果。
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