Welcome to Francis Academic Press

Academic Journal of Computing & Information Science, 2019, 2(1); doi: 10.25236/AJCIS.010028.

The Growth Pattern of Pterosaurs Based on Logistic Growth Model

Author(s)

Chengxi Hong1, Jianping Cai2, *

Corresponding Author:
Jianping Cai
Affiliation(s)

1 General Education Center of Xiamen Huaxia University, Xiamen, 361024, China
2 Xiamen Huaxia university Information and Smart Electromechanical Engineering, Xiamen, 361024, China
*Corresponding author e-mail: 1243946863@qq.com

Abstract

Fictional dragon becomes a new member of the real world family. As an old member, human need to know its origin and evolution, in order to get along with each other in the earth. Based on incomplete data and reasonable assumptions, we analyze the growth pattern of pterosaurs to obtain the function relation between weight and wingspan. With the estimation of height ratio of Daenerys and Drogon, we estimate the wingspan of a 6 year old Drogon, which is 59 meters. Putting the wingspan data into weight-wingspan function, we obtain the weight of Drogon, which is 34.031 tons. The result is of importance to the research of food intake of dragons. Finally, we build a toy ecosystem for dragon and the principles for dynamics of system are well found.

Keywords

Pterosaurs, Logistic growth model, Weight estimation

Cite This Paper

Chengxi Hong, Jianping Cai, The Growth Pattern of Pterosaurs Based on Logistic Growth Model. Academic Journal of Computing & Information Science (2019) Vol. 2: 142-147. https://doi.org/10.25236/AJCIS.010028.

References

[1] Witton MP (2008) a new approach to determining pterosaur body mass and its implications for pterosaur flight. Zitteliana B28: 143–159.
[2] BENNETT, S. C. (2007b): Articulation and function of the pteroid bone of pterosaurs. – Journal of Vertebrate Paleontology, 27: 881–891.
[3] Chen Yuanqian, Hu Jianguo, Zhang Dongjie. DERIVATION OF LOGISTIC MODEL AND ITS SELF-REGRESSION METHOD. XJPG, 1996, 17(2): 150-155.
[4] Sutton R S , Maei H R , Precup D , et al. Fast gradient-descent methods for temporal-difference learning with linear function approximation[C]// Danyluk Et. 2009.