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

Author

Zhou Kang ; Yan Li ; De Garis, Hugo ; Kang, Li-shan

Author_Institution

Comput. Center, Wuhan Univ., China

Volume

1

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

737

Lastpage

742

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

Keywords

data mining; differential equations; evolutionary computation; fractals; natural sciences computing; neural nets; KDD; complex system; data mining; flood season; macroscopic level laws; microscopic level laws; multilevel multiscale evolutionary modeling system; natural fractals; observed time series modelling; observed time series prediction; ordinary differential equations; scientific data; scientific law discovery; submicroscopic level laws; sunspot series; Control systems; Differential equations; Discrete wavelet transforms; Evolutionary computation; Floods; Fractals; Laboratories; Microscopy; Predictive models; Time series analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1005565

Filename

1005565