2002 Fiscal Year Final Research Report Summary
Automatic Derivation of Nonlinear State Equations by Using Artificial Intelligence in Inverse Problem of Complex Systems
| Project/Area Number |
13680449
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | KYUSHU UNIVERSITY |
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Principal Investigator |
OKAMOTO Masahiro Faculty of Agriculture, Prof., 農学研究院, 教授 (40211122)
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| Project Period (FY) |
2001 – 2002
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| Keywords | inverse problem / evolutional algorithm / complex system / genetic programming / inference system / nonlinear dynamics / automatic derivation system / generalized mass action law |
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| Research Abstract |
Estimation of the interaction mechanisms among system components by using experimentally observed dynamic responses (time-courses) of some of the system components is generally referred to as "inverse problem". Especially in complex systems such as chaos, only a slight difference in interaction mechanism or in initial value of a system component causes a drastic change in time-course responses of system components.
In this study, given experimentally observed time series data of system components, we have developed the inference system of interaction mechanism among system components based on evolutional algorithm (genetic programming, genetic algorithm). This system allows an automatic derivation of simultaneous differential equations based on generalized mass action law (GMA) which realize the experimentally observed time course data of system components. We have performed the validation of this sytem in the cases of two and three dimensional simultaneous nonlinear differential equations.
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[Publications] Maki, Y., Ueda, T., Okamoto, M., Uematsu, N., Inamura, K., Uchida, K., Takahashi, N., Eguchi, Y.: "Inference of Genetic Network Using the Expression Profile Time Course Data of Mouse P19 Cells"Genome Informatics. 13. 382-383 (2002)
Description
「研究成果報告書概要(欧文)」より
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