Title :
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
fDate :
6/24/1905 12:00:00 AM
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;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1005565
