%0 Conference Paper %1 Kang:2002:IJCNN %A Kang, Zhou %A Li, Yan %A de Garis, Hugo %A Kang, Li-Shan %B Proceedings of the 2002 International Joint Conference on Neural Networks IJCNN'02 %C Hilton Hawaiian Village Hotel, Honolulu, Hawaii %D 2002 %I IEEE Press %K algorithms, genetic programming %P 737--742 %T A Multi-Level And Multi-Scale Evolutionary Modeling System For Scientific Data %X 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. %@ 0-7803-7278-6

@inproceedings{Kang:2002:IJCNN, 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 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.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Hilton Hawaiian Village Hotel, Honolulu, Hawaii}, author = {Kang, Zhou and Li, Yan and {de Garis}, Hugo and Kang, Li-Shan}, biburl = {https://www.bibsonomy.org/bibtex/2ba50b5e46b0694e8abd355d7f601982f/brazovayeye}, booktitle = {Proceedings of the 2002 International Joint Conference on Neural Networks IJCNN'02}, interhash = {edd9dcdba8e8b4c8f5d2b81f78202df3}, intrahash = {ba50b5e46b0694e8abd355d7f601982f}, isbn = {0-7803-7278-6}, keywords = {algorithms, genetic programming}, month = {12-17 May}, notes = {IJCNN 2002 Held in connection with the World Congress on Computational Intelligence (WCCI 2002)}, organisation = {IEEE}, pages = {737--742}, publisher = {IEEE Press}, publisher_address = {445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA}, timestamp = {2008-06-19T17:42:53.000+0200}, title = {A Multi-Level And Multi-Scale Evolutionary Modeling System For Scientific Data}, year = 2002 }