Our aim in this paper is to present the design and implementation of a new numerical method to solve a class of stochastic delay population models. Firstly, a stochastic predator-prey model with time-delay and white noise is established. And then, a numerical simulation method based on the Milstein method is proposed to simulate the stochastic population model. Finally, the numerical solutions of the population model are obtained by using MATLAB software. The simulation results show that the new numerical simulation method can truly reflect the persistence and extinction process of stochastic predator-prey model, and provide a reference for solving the numerical simulation of the similar population models.
Published in | Control Science and Engineering (Volume 2, Issue 1) |
DOI | 10.11648/j.cse.20180201.13 |
Page(s) | 27-35 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2019. Published by Science Publishing Group |
Time-Delay, White Noise, Stochastic Population Model, Numerical Simulation
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APA Style
Changyou Wang, Kaixiang Yang, Xingcheng Pu, Rui Li. (2019). Numerical Method of a Class of Stochastic Delay Population Models. Control Science and Engineering, 2(1), 27-35. https://doi.org/10.11648/j.cse.20180201.13
ACS Style
Changyou Wang; Kaixiang Yang; Xingcheng Pu; Rui Li. Numerical Method of a Class of Stochastic Delay Population Models. Control Sci. Eng. 2019, 2(1), 27-35. doi: 10.11648/j.cse.20180201.13
@article{10.11648/j.cse.20180201.13, author = {Changyou Wang and Kaixiang Yang and Xingcheng Pu and Rui Li}, title = {Numerical Method of a Class of Stochastic Delay Population Models}, journal = {Control Science and Engineering}, volume = {2}, number = {1}, pages = {27-35}, doi = {10.11648/j.cse.20180201.13}, url = {https://doi.org/10.11648/j.cse.20180201.13}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cse.20180201.13}, abstract = {Our aim in this paper is to present the design and implementation of a new numerical method to solve a class of stochastic delay population models. Firstly, a stochastic predator-prey model with time-delay and white noise is established. And then, a numerical simulation method based on the Milstein method is proposed to simulate the stochastic population model. Finally, the numerical solutions of the population model are obtained by using MATLAB software. The simulation results show that the new numerical simulation method can truly reflect the persistence and extinction process of stochastic predator-prey model, and provide a reference for solving the numerical simulation of the similar population models.}, year = {2019} }
TY - JOUR T1 - Numerical Method of a Class of Stochastic Delay Population Models AU - Changyou Wang AU - Kaixiang Yang AU - Xingcheng Pu AU - Rui Li Y1 - 2019/01/14 PY - 2019 N1 - https://doi.org/10.11648/j.cse.20180201.13 DO - 10.11648/j.cse.20180201.13 T2 - Control Science and Engineering JF - Control Science and Engineering JO - Control Science and Engineering SP - 27 EP - 35 PB - Science Publishing Group SN - 2994-7421 UR - https://doi.org/10.11648/j.cse.20180201.13 AB - Our aim in this paper is to present the design and implementation of a new numerical method to solve a class of stochastic delay population models. Firstly, a stochastic predator-prey model with time-delay and white noise is established. And then, a numerical simulation method based on the Milstein method is proposed to simulate the stochastic population model. Finally, the numerical solutions of the population model are obtained by using MATLAB software. The simulation results show that the new numerical simulation method can truly reflect the persistence and extinction process of stochastic predator-prey model, and provide a reference for solving the numerical simulation of the similar population models. VL - 2 IS - 1 ER -