Functional Structure Modeling Based on Fuzzy Inference

Ying Ying Yin; Jin Ying Li

doi:10.4028/www.scientific.net/AMM.651-653.2310

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 651-653Functional Structure Modeling Based on Fuzzy...

Functional Structure Modeling Based on Fuzzy Inference

Abstract:

A method that use fuzzy inference theory to infer the functional structure model is presented. Is based on fuzzy inference theory of artificial intelligence area, analyze and study the measured data, then extract plant growth rule and growth function. When constructing functional structure that can reflect the impact of the environment, the influence of environment was taken into full account. The source and sink organs respond the surrounding virtual environment according to its inbuilt growth function, and produce, allocate and consume assimilates as well as update the L-grammar representing the plant structure, and at last produce the plant that adapt to present virtual environment. The simulation test result show that the model can accurately extract the growth rule, construct right growth function, and vividly reflect the impact of environment.

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Periodical:

Applied Mechanics and Materials (Volumes 651-653)

Pages:

2310-2313

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.651-653.2310

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Online since:

September 2014

Authors:

Ying Ying Yin, Jin Ying Li

Keywords:

Functional Structure Model, Fuzzy Inference, Virtual Plant

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References

[1] GODIN C, SINOQUET H. Functional structural plant modeling[J]. New Phytologist, 2005, 166(3): 705-708.

DOI: 10.1111/j.1469-8137.2005.01445.x

Google Scholar

[2] BORNHOFEN S, LATTAUD C. Competition and evolution in virtual plant communities: a new modeling approach[J]. Natural Computing. 2008, 8(2): 249-385.

DOI: 10.1007/s11047-008-9089-5

Google Scholar

[3] MUYSKENS M. The fluorescence of lignum nephriticum: a flash back to the past and a simple demonstration of natural substance fluorescence[J]. Journal of Chemical Education, 2006, 83(5): 765-768.

DOI: 10.1021/ed083p765

Google Scholar

[4] LIU YING-HUI, JIA HAI-KUN, GAO QIONG. Review on researches of photo-assimilates partitioning and its models[J]. Acta Ecologica Sinica, 2006, 26(6): 1981-(1992).

Google Scholar

[5] WOLFGANG J. Quasi-Monte carlo sampling to improve the efficiency of monte carlo EM[J]. Computational Statistics and Data Analysis, 2005, 48(4): 685-701.

DOI: 10.1016/j.csda.2004.03.019

Google Scholar