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Complexity
Volume 2019, Article ID 3426974, 14 pages
https://doi.org/10.1155/2019/3426974
Research Article

Numerical Simulation of a Class of Three-Dimensional Kolmogorov Model with Chaotic Dynamic Behavior by Using Barycentric Interpolation Collocation Method

1Institute of Computer Information Management, Inner Mongolia University of Finance and Economics, Hohhot 010070, China
2Department of Mathematics, Inner Mongolia University of Technology, Hohhot 010051, China
3Institute of Economics and Management, Jining Normal University, Jining 012000, Inner Mongolia, China

Correspondence should be addressed to Wei Zhang; moc.361@wzyxfsnj

Received 5 January 2019; Revised 13 February 2019; Accepted 3 March 2019; Published 9 April 2019

Academic Editor: Giacomo Innocenti

Copyright © 2019 Mingjing Du et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper numerically simulates three-dimensional Kolmogorov model with chaotic dynamic behavior by barycentric Lagrange interpolation collocation method. Some numerical examples are studied for finding some new chaotic behaviors and demonstrating some existing chaotic dynamic behaviors of the Kolmogorov model. Results obtained by the present method indicate that the method has merits of small operations and good numerical stability.

1. Introduction

J.P. Berrut [1, 2] introduced barycentric Lagrange interpolation and studied its numerical stability and convergence. Barycentric Lagrange interpolation is unconditionally stable at the Chebyshev points. S.P. Li, Z.Q. Wang [3, 4] gave some algorithms of barycentric Lagrange interpolation collocation method (BLICM). Some authors [3, 58] solved all sorts of equations and showed the BLICM has merits of small operations and high precision (see [3, 4, 9]). This paper numerically simulates some three-dimensional Kolmogorov models with chaotic dynamic behavior. The purpose of this paper is to find some new chaotic behaviors and verify the existing chaotic dynamic behaviors by the BLICM.

Three-dimensional Kolmogorov models comprise a significant class of ecological models that are used widely in ecology to represent the dynamic behavior of prey and predators, which are expressed in the following form:where represents the population density of the species and represents the per capita growth rate of the species.

In ecology, the most frequently used model is the Lotka-Volterra system; that is, each per capita growth function is affine and chosen as the logistic growth. In this circumstance, model (1) reads as

Model (2) is a totally competitive system if all parameters are positive.

There are several famous functional responses in the Kolmogorov model which are referred to as Holling type I, type II, type III, type IV, Monod-Haldane type, Hassel-Verley type, Beddington-DeAngelis type functional response, etc.

In this paper, we consider the following three-dimensional Kolmogorov model with functional response:with the following initial condition:where represent the population density of the species and is the known functional response. are known constants. are unknown functions of time .

Many researchers [1020] studied the dynamics of three dimensional Kolmogorov model with different type of functional response in theory. They found some chaotic dynamics [13, 1823] of the three-dimensional Kolmogorov model. Chaos and hyperchaos exist in many natural processes and are one of the main contents of nonlinear science research. Although many kinds of numerical methods of the Kolmogorov model have been announced, simple and efficient methods have always been the direction that scholars strive to pursue. This paper suggests the BLICM to solve the three-dimensional Kolmogorov model. Model (3) is adopted as an example to elucidate the solution process.

2. Barycentric Lagrange Interpolation Collocation Method

First of all, we discrete computational interval by Chebyshev points into and construct following linear iterative format of model (3):

The format (5) is convergent; then , , .

Next, we transform format (5) into the following linear algebraic equations.where is order matrix. is order unit matrix and is a symbol of diagonal matrix composed of vectors. is, respectively, barycentric interpolation primary function and is center of gravity Lagrange interpolation weight. The vector

The vector

The last and the first line of equations (6) are replaced separately by the equation of the initial condition (9) in turn.

So, we can get a numerical solution of (3) and (4).

3. Numerical Experiments

In this section, some numerical examples are studied to demonstrate the accuracy of the present method. The examples are computed using MatlabR2017a. In numerical experiments, the number of nodes . The accuracy of iteration control and the initial iteration value .

Experiment 1. We consider the following three-species food chain model [18]:where is real parameters, which satisfy the initial condition Results of Experiment 1 are given in Figures 1-2.
Figure 1 is obtained by using the current method with . Among them, is the time series plot; is the phase diagram of ; is the phase diagram of ; is the graph projected on -plane; is the three-dimensional space graph. Figure 2 is obtained by using the current method with . is the time series plot; is the phase diagram of ; is the three-dimensional space graph; is the graph projected on -plane; is the the graph projected on -plane; is the graph projected on -plane.
Figure 1 gets some new chaotic behaviors. Figure 2 verifies the existing chaotic dynamic behaviors [18]. Our study suggests that model (10) will go chaotic when the rate of the self-reproduction of the prey is large. Our numerical results are in good agreement with the theory [18].

Figure 1: The three species food chain model for Experiment 1 with : time series plot; phase diagram of ; phase diagram of ; projected on the -plane; on the three-dimensional space.
Figure 2: The three-species food chain model for Experiment 1 with : time series plot; phase diagram of ; on the three-dimensional space; projected on the -plane; projected on the -plane; projected on the -plane.

Experiment 2. We consider the following tritrophic food chain model [19]:In comparison with [19], we get better numerical results; results of Experiment 2 are given in Figures 3-4. Our numerical results are in good agreement with the theory [19].
Figures 3 and 4 are obtained by using the current method with the initial conditions : and the initial conditions : , respectively. and are the time series plot of ; and are the phase diagram of ; and are the three-dimensional space graph; and are the graph projected on -plane; and are the graph projected on -plane; and are the graph projected on -plane. It can be seen that the change of initial conditions leads to significant changes in the time series diagrams of , and .

Figure 3: A tritrophic food web system for Experiment 2 with the initial condition : time series plot of ; phase diagram of ; on the three-dimensional space; projected on the -plane; projected on the -plane; projected on the -plane.
Figure 4: A tritrophic food web system for Experiment 2 with the initial condition : time series plot of ; phase diagram of ; on the three-dimensional space; projected on the -plane; projected on the -plane; projected on the -plane.

Experiment 3. We consider the following tritrophic food chain model [20]:where is real parameters, which satisfy the initial condition Our numerical simulations and experimental results of [20] are confirmed. Results of Experiment 3 are given in Figure 5.
Figure 5 is obtained by using the current method. is time series plot and is the three-dimensional space graph with ; is time series plot and is the three-dimensional space graph with ; is time series plot and is the three-dimensional space graph with . We can see that, from the figure, the fluctuation range of at is very large when ; the fluctuation range of at is obviously reduced when ; at almost all tend to be stationary when .

Figure 5: Time series plots and the three-dimensional space graphs of the tritrophic food chain model for Experiment 3 with different parameters: and ; and ; and .

Experiment 4. We consider the following model with Beddington-DeAngelis-type functional response [21]:where , and are real parameters, which satisfy the initial condition We choose the parameters . Results of Experiment 4 are given in Figures 6-7.
Figure 6 is time series plots and the three-dimensional space graphs of a predator-prey model with Beddington-DeAngelis type functional response for Experiment 4 with different parameters obtained by using the current method. is time series plot and is the three-dimensional space graph with ; is time series plot and is the three-dimensional space graph with ; is time series plot and is the three-dimensional space graph with . Figure 7 is obtained by using the current method with and , respectively. and are the graph projected on -plane; and are the graph projected on -plane; and are the graph projected on -plane.

Figure 6: Time series plots and the three-dimensional space graphs of Experiment 4 with different parameters: and ; and ; and .
Figure 7: The results of Experiment 4: with and with , and projected on the -plane; and projected on the -plane; and projected on the -plane.

Experiment 5. We consider the following model with Monod-Haldane type response function [22]:where , and are real parameters, which satisfy the initial condition We choose the parameter .
Our numerical simulation results are in good agreement with the theory [22]. We give some new chaotic behaviors. Results of Experiment 5 are given in Figures 8-9.
Figure 8 is time series plots and the three-dimensional space graphs of a predator-prey system with Monod-Haldane type response function for Experiment 5 with different parameters obtained by using the current method. is time series plot and is the three-dimensional space graph with ; is time series plot and is the three-dimensional space graph with ; is time series plot and is the three-dimensional space graph with . Figure 9 is obtained by using the current method with and , respectively. and are the graph projected on -plane; and are the graph projected on -plane; and are the graph projected on -plane.

Figure 8: Time series plots and the three-dimensional space graphs of Experiment 5 with different parameters: and ; and ; and .
Figure 9: The results of Experiment 5: with and with , and projected on the -plane; and projected on the -plane; and projected on the -plane.

Experiment 6. We consider the following model with a Watt-type functional response [23]:Our numerical simulation results are in good agreement with the theory [23]. Compared with [23], we get better numerical results and give some new chaotic behaviors. Results of Experiment 6 are given in Figures 1015. We choose the parameter; see Tables 1-2.
Figures 1012 are obtained by using the current method. , , and are the time series plot of ; , , and are the phase diagram of ; , , and are the phase diagram of ; , , and are the graph projected on -plane; , , and are the three-dimensional space graph. Figures 1315 are obtained by using the current method. , , and are the time series plot of ; , , and are the phase diagram of ; , , and are the phase diagram of ; , , and are the graph projected on -plane; , , and are the three-dimensional space graph.

Table 1: Parameters used in Experiment 6.
Table 2: Parameters used in Experiment 6.
Figure 10: The results of Experiment 6:   time series plot of ; phase diagram of ; phase diagram of ; projected on the -plane; on the three-dimensional space.
Figure 11: The results of Experiment 6: time series plot of ; phase diagram of ; phase diagram of ; projected on the -plane; on the three-dimensional space.
Figure 12: The results of Experiment 6: time series plot of ; phase diagram of ; phase diagram of ; projected on the -plane; on the three-dimensional space.
Figure 13: The results of Experiment 6: time series plot of ; phase diagram of ; phase diagram of ; projected on the -plane; on the three-dimensional space.
Figure 14: The results of Experiment 6: time series plot of ; phase diagram of ; phase diagram of ; projected on the -plane; on the three-dimensional space.
Figure 15: The results of Experiment 6: time series plot of ; phase diagram of ; phase diagram of ; projected on the -plane; on the three-dimensional space.

4. Conclusions and Remarks

In this paper, the three dimensional Kolmogorov model was solved by using the barycentric Lagrange interpolation collocation method. These numerical experiments illustrate that numerical results of the present method are the same as experimental results.

All computations are performed by the MatlabR2007b software packages.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this article.

Authors’ Contributions

Mingjing Du carried out the main part of this manuscript. Wei Zhang obtained data and analyze numerical simulation results, which greatly improved the quality of the paper. Junmei Li carried out numerical simulation and gave some numerical results. Yulan Wang made the program of the barycentric interpolation collocation method in the revised version and corrected the derived results. All authors read and approved the final manuscript. When we resubmitted the revised version, we forgot to change the author list.

Acknowledgments

The authors thank the reviewers for their valuable suggestions, which greatly improved the quality of the paper. This paper is supported by the Natural Science Foundation of Inner Mongolia , Inner Mongolia Maker Collaborative Innovation Center of Jining Normal University, and the National Natural Science Foundation of China .

References

  1. J. P. Berrut and L. N. Trefethen, “Barycentric lagrange interpolation,” SIAM Review, vol. 46, no. 3, pp. 501–517, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  2. N. J. Higham, “The numerical stability of barycentric Lagrange interpolation,” IMA Journal of Numerical Analysis (IMAJNA), vol. 24, no. 4, pp. 547–556, 2004. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  3. S. P. Li and Z. Q. Wang, Barycentric Interpolation Collocation Method for Nonlinear Problems, National Defense Industry Press, Beijing, China, 2015.
  4. S. P. Li and Z. Q. Wang, High-Precision Non-Grid Center of Gravity Interpolation Collocation Method: Algorithm, Program And Engineering Application, Science Press, Beijing China, 2012.
  5. H. Wu, Y. Wang, and W. Zhang, “Numerical solution of a class of nonlinear partial differential equations by using barycentric interpolation collocation method,” Mathematical Problems in Engineering, vol. 2018, Article ID 7260346, 10 pages, 2018. View at Publisher · View at Google Scholar · View at MathSciNet
  6. F. Liu, Y. Wang, and S. Li, “Barycentric interpolation collocation method for solving the coupled viscous Burgers' equations,” International Journal of Computer Mathematics, vol. 95, no. 11, pp. 2162–2173, 2018. View at Publisher · View at Google Scholar · View at MathSciNet
  7. H. Liu, J. Huang, Y. Pan, and J. Zhang, “Barycentric interpolation collocation methods for solving linear and nonlinear high-dimensional Fredholm integral equations,” Journal of Computational and Applied Mathematics, vol. 327, pp. 141–154, 2018. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  8. W.-H. Luo, T.-Z. Huang, X.-M. Gu, and Y. Liu, “Barycentric rational collocation methods for a class of nonlinear parabolic partial differential equations,” Applied Mathematics Letters, vol. 68, pp. 13–19, 2017. View at Publisher · View at Google Scholar · View at MathSciNet
  9. S. P. Li and Z. Q. Wang, “Barycentric interpolation collocation method for solving nonlinear vibration problems,” Noise Vibration Control, vol. 28, pp. 49–52, 2018. View at Google Scholar
  10. X. Zhang, C. Shan, Z. Jin, and H. Zhu, “Complex dynamics of epidemic models on adaptive networks,” Journal of Differential Equations, vol. 266, no. 1, pp. 803–832, 2019. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  11. N. Juneja and K. Agnihotri, “Conservation of a predator species in SIS prey-predator system using optimal taxation policy,” Chaos, Solitons & Fractals, vol. 116, pp. 86–94, 2018. View at Publisher · View at Google Scholar · View at MathSciNet
  12. R. D. Parshad, N. Kumari, A. R. Kasimov, and H. Ait Abderrahmane, “Turing patterns and long-time behavior in a three-species food-chain model,” Mathematical Biosciences, vol. 254, pp. 83–102, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  13. P. Paul, B. Ghosh, and T. K. Kar, “Impact of species enrichment and fishing mortality in three species food chain models,” Communications in Nonlinear Science and Numerical Simulation, vol. 29, no. 1-3, pp. 208–223, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  14. V. L. Kolmogorov, V. P. Fedotov, and L. F. Spevak, “A mathematical model for the formation and development of defects in metals,” Studies in Applied Mechanics, vol. 45, no. C, pp. 51–60, 1997. View at Publisher · View at Google Scholar · View at Scopus
  15. Z.-P. Ma and Y.-X. Wang, “Bifurcation of positive solutions for a three-species food chain model with diffusion,” Computers & Mathematics with Applications. An International Journal, vol. 74, no. 12, pp. 3271–3282, 2017. View at Publisher · View at Google Scholar · View at MathSciNet
  16. T. Zeng, Z. Teng, Z. Li, and J. Hu, “Stability in the mean of a stochastic three species food chain model with general L[formula omitted]vy jumps,” Chaos, Solitons and Fractals, vol. 106, pp. 258–265, 2018. View at Publisher · View at Google Scholar · View at MathSciNet
  17. S.-B. Hsu, S. Ruan, and T.-H. Yang, “Analysis of three species Lotka-Volterra food web models with omnivory,” Journal of Mathematical Analysis and Applications, vol. 426, no. 2, pp. 659–687, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  18. R. K. Upadhyay, “Chaotic behaviour of population dynamic systems in ecology,” Mathematical and Computer Modelling, vol. 32, no. 9, pp. 1005–1015, 2000. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  19. A. Priyadarshi and S. Gakkhar, “Dynamics of Leslie-Gower type generalist predator in a tri-trophic food web system,” Communications in Nonlinear Science and Numerical Simulation, vol. 18, no. 11, pp. 3202–3218, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  20. D. Jana, R. Agrawal, and R. K. Upadhyay, “Top-predator interference and gestation delay as determinants of the dynamics of a realistic model food chain,” Chaos, Solitons & Fractals, vol. 69, pp. 50–63, 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. S. Khajanchi, “Dynamic behavior of a Beddington-DeAngelis type stage structured predator-prey model,” Applied Mathematics and Computation, vol. 244, pp. 344–360, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. S. Khajanchi, “Modeling the dynamics of stage-structure predator-prey system with Monod–Haldane type response function,” Applied Mathematics and Computation, vol. 302, pp. 122–143, 2017. View at Publisher · View at Google Scholar · View at Scopus
  23. X. Lin, Y. Jiang, and X. Wang, “Existence of periodic solutions in predator-prey with Watt-type functional response and impulsive effects,” Nonlinear Analysis. Theory, Methods & Applications. An International Multidisciplinary Journal, vol. 73, no. 6, pp. 1684–1697, 2010. View at Publisher · View at Google Scholar · View at MathSciNet