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

被引：0
作者：
Kang, Z [1 ]
Li, Y [1 ]
de Garis, H [1 ]
Kang, LS [1 ]
机构：
[1] Wuhan Univ, Computat Ctr, Wuhan 430072, Peoples R China
来源：
PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3 | 2002年
关键词：
D O I：
10.1109/IJCNN.2002.1005565
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 multi-level 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.
引用
收藏
页码：737 / 742
页数：4
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