Skip to main content
Log in

Relationship between time-instant number and precision of ZeaD formulas with proofs

  • Original Paper
  • Published:
Numerical Algorithms Aims and scope Submit manuscript

Abstract

Recently, zeroing dynamics (ZD) method has shown wonderful effect in solving time-varying problems. Generally, the continuous-time ZD model should be discretized as a discrete-time algorithm for numerical computation. A good choice is to apply Zhang et al. discretization (ZeaD) formulas, which are effective time-discretization formulas. Actually, ZeaD formulas can also be applied to obtaining various discrete-time algorithms, not only the ZD algorithms. In the previous work, some piecemeal ZeaD formulas have been proposed and investigated. However, the relationship between the time-instant number and precision of ZeaD formulas is not found, which is emphatically investigated in this paper. Specifically, the ZeaD formulas from two to nine instants are investigated, and the general ZeaD formula groups are studied. Two-instant and three-instant ZeaD formula groups have linear precision at most. Four-instant ZeaD formula group has quadratic precision at most. Five-instant and six-instant ZeaD formula groups have cubic precision at most. Seven-instant and eight-instant ZeaD formula groups have quartic precision at most. Nine-instant ZeaD formula group has quintic precision at most. Theoretical analyses are presented to substantiate the relationship. Moreover, the ZeaD formulas as well as ZD method are applied to solving time-varying quadratic optimization problem, and the numerical results verify the effectiveness of ZeaD formulas.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Na, J., Xing, Y., Costa-Castello, R.: Adaptive estimation of time-varying parameters with application to roto-magnet plant. IEEE Trans. Syst., Man, Cybern. -Syst. (to be published. https://doi.org/10.1109/TSMC.2018.2882844https://doi.org/10.1109/TSMC.2018.2882844)

  2. Liu, Y.J., Gong, M., Liu, L., Tong, S., Chen, C.L.P.: Fuzzy observer constraint based on adaptive control for uncertain nonlinear MIMO systems with time-varying state constraints. IEEE Trans. Cybern. (to be published. https://doi.org/10.1109/TCYB.2019.2933700)

  3. Li, W.: A recurrent neural network with explicitly definable convergence time for solving time-variant linear matrix equations. IEEE Trans. Ind. Inform. 14(12), 5289–5298 (2018)

    Article  Google Scholar 

  4. Stanimirovic, P.S., Katsikis, V.N., Li, S.: Higher-order ZNN dynamics. Neural Process Lett. 51(12), 697–721 (2020)

    Article  Google Scholar 

  5. Stanimirovic, P.S., Katsikis, V.N., Li, S.: Integration enhanced and noise tolerant ZNN for computing various expressions involving outer inverses. Neurocomputing 329, 129–143 (2019)

    Article  Google Scholar 

  6. Tan, Z., Xiao, L., Chen, S., Lv, X.: Noise-tolerant and finite-time convergent ZNN models for dynamic matrix Moore-Penrose inversion. IEEE Trans. Ind. Inform. 16(3), 1591–1601 (2020)

    Article  Google Scholar 

  7. Guo, D., Yan, L., Nie, Z.: Design, analysis, and representation of novel five-step DTZD algorithm for time-varying nonlinear optimization. IEEE Trans. Neural Netw. Learn. Syst. 29(9), 4248–4260 (2018)

    Article  Google Scholar 

  8. Zhang, Z., Lu, Y., Zheng, L., Li, S., Li, Y.: A new varying-parameter convergent-differential neural-network for solving time-varying convex QP problem constrained by linear-equality. IEEE Trans. Autom. Control 63(12), 4110–4125 (2018)

    Article  MathSciNet  Google Scholar 

  9. Zhang, Y., Yi, C.: Zhang Neural Networks and Neural-Dynamic Method. Nova Science Publishers Inc, New York (2011)

    Google Scholar 

  10. Zhang, Y., Xiao, L., Ruan, G., Li, Z.: Continuous and discrete time Zhang dynamics for time-varying 4th root finding. Numer. Algorithms 57(1), 35–51 (2011)

    Article  MathSciNet  Google Scholar 

  11. Guo, D., Nie, Z., Yan, L.: Theoretical analysis, numerical verification and geometrical representation of new three-step DTZD algorithm for time-varying nonlinear equations solving. Neurocomputing 214, 516–526 (2016)

    Article  Google Scholar 

  12. Guo, D., Lin, X., Su, Z., Sun, S., Huang, Z.: Design and analysis of two discrete-time ZD algorithms for time-varying nonlinear minimization. Numer. Algorithms 77, 23–36 (2018)

    Article  MathSciNet  Google Scholar 

  13. Qiu, B., Zhang, Y., Guo, J., Yang, Z., Li, X.: New five-step DTZD algorithm for future nonlinear minimization with quartic steady-state error pattern. Numer. Algorithms 81, 1043–1065 (2019)

    Article  MathSciNet  Google Scholar 

  14. Chen, D., Li, S., Lin, F.J., Wu, Q.: New super-twisting zeroing neural-dynamics model for tracking control of parallel robots: A finite-time and robust solution. IEEE Trans. Cybern. 50(6), 2651–2660 (2020)

    Article  Google Scholar 

  15. Li, S., Guo, Y.: Discrete-time consensus filters for average tracking of time-varying inputs on directed switching graphs. Asian J. Control 20 (2), 919–934 (2018)

    Article  MathSciNet  Google Scholar 

  16. Zhang, Y., Li, S., Liao, L.: Consensus of high-order discrete-time multiagent systems with switching topology. IEEE Trans. Syst. Man Cybern. -Syst. (to be published. https://doi.org/10.1109/TSMC.2018.2882558https://doi.org/10.1109/TSMC.2018.2882558)

  17. Chen, J., Zhang, Y.: Continuous and discrete zeroing neural dynamics handling future unknown-transpose matrix inequality as well as scalar inequality of linear class. Numer. Algorithms 83, 529–547 (2020)

    Article  MathSciNet  Google Scholar 

  18. Liu, Y.J., Li, S., Tong, S., Chen, C.L.P.: Adaptive reinforcement learning control based on neural approximation for nonlinear discrete-time systems with unknown nonaffine dead-zone input. IEEE Trans. Neural Netw. Learn. Syst. 30(1), 295–305 (2019)

    Article  Google Scholar 

  19. Mathews, J.H., Fink, K.D.: Numerical Methods Using Matlab. Prentice-Hall, Englewood Cliffs (2004)

    Google Scholar 

  20. Zhang, Y., Li, Z., Guo, D., Ke, Z., Chen, P.: Discrete-time ZD, GD and NI for solving nonlinear time-varying equations. Numer. Algorithms 64 (4), 721–740 (2013)

    Article  MathSciNet  Google Scholar 

  21. Jin, L., Zhang, Y.: Continuous and discrete Zhang dynamics for real-time varying nonlinear optimization. Numer. Algorithms 73(1), 115–140 (2016)

    Article  MathSciNet  Google Scholar 

  22. Li, J., Mao, M., Uhlig, F., Zhang, Y.: A 5-instant finite difference formula to find discrete time-varying generalized matrix inverses, matrix inverses, and scalar reciprocals. Numer. Algorithms 81, 609–629 (2019)

    Article  MathSciNet  Google Scholar 

  23. Zhang, Y., Liu, X., Ling, Y., Yang, M., Huang, H.: Continuous and discrete zeroing dynamics models using JMP function array and design formula for solving time-varying Sylvester-transpose matrix inequality. Numer. Algorithms (to be published. https://doi.org/10.1007/s11075-020-00946-1)

  24. Zhang, Y., Zhu, M., Hu, C., Li, J., Yang, M.: Euler-precision general-form of Zhang others discretization (ZeaD) formulas, derivation, and numerical experiments. In: Proc. of Chinese Control and Decision Conference (CCDC), Shenyang, China, pp. 6262–6267 (2018)

  25. Hu, C., Kang, X., Zhang, Y.: Three-step general discrete-time Zhang neural network design and application to time-variant matrix inversion. Neurocomputing 306, 108–118 (2018)

    Article  Google Scholar 

  26. Li, J., Zhang, Y., Mao, M.: General square-pattern discretization formulas via second-order derivative elimination for zeroing neural network illustrated by future optimization. IEEE Trans. Neural Netw. Learn. Syst. 30(3), 891–901 (2019)

    Article  MathSciNet  Google Scholar 

  27. Zhang, Y., He, L., Hu, C., Guo, J., Li, J., Shi, Y.: General four-step discrete-time zeroing and derivative dynamics applied to time-varying nonlinear optimization. J. Comput. Appl. Math. 347, 314–329 (2019)

    Article  MathSciNet  Google Scholar 

  28. Hu, C., Zhang, Y., Kang, X.: General and improved five-step discrete-time zeroing neural dynamics solving linear time-varying matrix equation with unknown transpose. Neural Process Lett. 51, 1715–1730 (2020)

    Article  Google Scholar 

  29. Zhang, Y., Guo, J., He, L., Shi, Y., Hu, C.: Any ZeaD formula of six instants having no quartic or higher precision with proof. In: Proc. of International Conference on Systems and Informatics (ICSAI), Nanjing, China, pp. 681–685 (2018)

  30. Sun, M., Wang, Y.: General five-step discrete-time Zhang neural network for time-varying nonlinear optimization. Bull. Malays. Math. Sci. Soc. 43, 1741–1760 (2020)

    Article  MathSciNet  Google Scholar 

  31. Yang, M., Zhang, Y., Hu, H., Qiu, B.: General 7-instant DCZNN model solving future different-level system of nonlinear inequality and linear equation. IEEE Trans. Neural Netw. Learn. Syst. 31(9), 3204–3214 (2020)

    Article  MathSciNet  Google Scholar 

  32. Suli, E., Mayers, D.F.: An Introduction to Numerical Analysis. Cambridge University Press, Cambridge (2003)

    Book  Google Scholar 

  33. Oppenheim, A.V.: Discrete-time Signal Processing, 3rd edn. Pearson Higher Education, New Jersey (2010)

    Google Scholar 

  34. Ogata, K.: Modern Control Engineering, 4th edn. Prentice Hall, New Jersey (2001)

    MATH  Google Scholar 

  35. Qin, S., Xue, X.: A two-layer recurrent neural network for nonsmooth convex optimization problems. IEEE Trans. Neural Netw. Learn. Syst. 26(6), 1149–1160 (2015)

    Article  MathSciNet  Google Scholar 

  36. Qin, S., Le, X., Wang, J.: A neurodynamic optimization approach to bilevel linear programming. IEEE Trans. Neural Netw. Learn. Syst. 28(11), 2580–2591 (2017)

  37. Liu, N., Qin, S.: A neurodynamic approach to nonlinear optimization problems with affine equality and convex inequality constraints. Neural Netw. 109, 147–158 (2019)

    Article  Google Scholar 

  38. Liu, N., Qin, S.: A novel neurodynamic approach to constrained complex-variable pseudoconvex optimization. IEEE Trans. Cybern. 49(11), 3946–3956 (2019)

    Article  Google Scholar 

  39. Qin, S., Yang, X., Xue, X., Song, J.: A one-layer recurrent neural network for pseudoconvex optimization problems with equality and inequality constraints. IEEE Trans. Cybern. 47(10), 3063–3074 (2017)

    Article  Google Scholar 

  40. Xiao, L.: A nonlinearly-activated neurodynamic model and its finite-time solution to equality-constrained quadratic optimization with nonstationary coefficients. Appl. Soft Comput. 40, 252–259 (2016)

    Article  Google Scholar 

  41. Xiao, L., Li, K., Duan, M.: Computing time-varying quadratic optimization with finite-time convergence and noise tolerance: A unified framework for zeroing neural network. IEEE Trans. Neural Netw. Learn. Syst. 30(11), 3360–3369 (2019)

    Article  MathSciNet  Google Scholar 

  42. Xiao, L., Li, S., Yang, J., Zhang, Z.: A new recurrent neural network with noise-tolerance and finite-time convergence for dynamic quadratic minimization. Neurocomputing 285, 125–132 (2018)

    Article  Google Scholar 

  43. Miao, P., Shen, Y., Huang, Y., Wang, Y.W.: Solving time-varying quadratic programs based on finite-time Zhang neural networks and their application to robot tracking. Neural Comput. Appl. 26(3), 693–703 (2015)

    Article  Google Scholar 

  44. Zhang, Y., Gong, H., Yang, M., Li, J., Yang, X.: Stepsize range and optimal value for Taylor-Zhang discretization formula applied to zeroing neurodynamics illustrated via future equality-constrained quadratic programming. IEEE Trans. Neural Netw. Learn. Syst. 30(3), 959–966 (2019)

    Article  MathSciNet  Google Scholar 

  45. Zhang, Y., Qi, Z., Li, J., Qiu, B., Yang, M.: Stepsize domain confirmation and optimum of ZeaD formula for future optimization. Numer. Algorithms 81, 561–574 (2018)

    Article  MathSciNet  Google Scholar 

Download references

Funding

The work is aided by the National Natural Science Foundation of China (with numbers 61976230 and 61673402), the Natural Science Foundation of Guangdong Province (with number 2017A030311029), the Project Supported by Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (with number 2018), the Research Fund Program of Guangdong Key Laboratory of Modern Control Technology (with number 2017B030314165), and also the Key-Area Research and Development Program of Guangzhou (with number 202007030004).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Haifeng Hu.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix: 1. Proof of three-instant ZeaD formula group

Evidently, when m = 3, three-instant ZeaD formula can be presented as

$$ \dot{x}_{k}=a_{2}\frac{x_{k+1}}{\tau}+a_{1}\frac{x_{k}}{\tau}+a_{0}\frac{x_{k-1}}{\tau}+O(\tau^{p}). $$

According to (2), if p = 2, one has

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} a_{2}+a_{1}+a_{0}=0,\\ a_{2}-a_{0}=1,\\ a_{2}+a_{0}=0, \end{array} \right. \end{array} $$

i.e.,

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} a_{2}=1/2,\\ a_{1}=0,\\ a_{0}=-1/2. \end{array} \right. \end{array} $$

In this case, the characteristic equation is

$$ P_{2}(\gamma)=\frac{1}{2}\gamma^{2}-\frac{1}{2}=0, $$

whose roots are γ0 = 1 and γ1 = − 1. Hence, the synthesized formula is not enough 0-stable [32], i.e., three-instant ZeaD formula cannot have O(τ2) precision.

Meanwhile, if p = 1, one has

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} a_{2}+a_{1}+a_{0}=0,\\ a_{2}-a_{0}=1, \end{array} \right. \end{array} $$

i.e.,

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} a_{2}=a_{0}+1,\\ a_{1}=-2a_{0}-1. \end{array} \right. \end{array} $$

In this case, the characteristic equation is

$$ P_{2}(\gamma)=(a_{0}+1)\gamma^{2}-(2a_{0}+1)\gamma+a_{0}=0. $$

By applying bilinear transformation [33, 34], i.e., γ = (ω + 1)/(ω − 1), one gets

$$ P_{2}(\omega)=2\omega+4a_{0}+2=0. $$

From Routh stability criterion [33, 34], the 0-stable condition is a0 > − 1/2. Besides, a0≠ 0 should be satisfied. The proof is thus completed.

Appendix: 2. Proof of four-instant ZeaD formula group

When m = 4, four-instant ZeaD formula can be presented as

$$ \dot{x}_{k}=a_{3}\frac{x_{k+1}}{\tau}+a_{2}\frac{x_{k}}{\tau}+a_{1}\frac{x_{k-1}}{\tau}+a_{0}\frac{x_{k-2}}{\tau}+O(\tau^{p}). $$

According to (2), if p = 3, one has

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} a_{3}+a_{2}+a_{1}+a_{0}=0,\\ a_{3}-a_{1}-2a_{0}=1,\\ a_{3}+a_{1}+4a_{0}=0,\\ a_{3}-a_{1}-8a_{0}=0, \end{array} \right. \end{array} $$

i.e.,

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} a_{3}=1/3,\\ a_{2}=1/2,\\ a_{1}=-1,\\ a_{0}=1/6. \end{array} \right. \end{array} $$

In this case, the characteristic equation is

$$ P_{3}(\gamma)=\frac{1}{3}\gamma^{3}+\frac{1}{2}\gamma^{2}-\gamma+\frac{1}{6}=0, $$

whose roots are γ0 = 1, \(\gamma _{1}=(\sqrt {33}-5)/4\), and \(\gamma _{2}=(-\sqrt {33}-5)/4\). Evidently, |γ2| > 1. Hence, the synthesized formula is not 0-stable [32], i.e., four-instant ZeaD formula cannot have O(τ3) precision.

Meanwhile, if p = 2, one has

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} a_{3}+a_{2}+a_{1}+a_{0}=0,\\ a_{3}-a_{1}-2a_{0}=1,\\ a_{3}+a_{1}+4a_{0}=0, \end{array} \right. \end{array} $$

i.e.,

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} a_{3}=-a_{0}+1/2,\\ a_{2}=3a_{0},\\ a_{1}=-3a_{0}-1/2. \end{array} \right. \end{array} $$

In this case, the characteristic equation is

$$ P_{3}(\gamma)=\left( -a_{0}+\frac{1}{2}\right)\gamma^{3}+3a_{0}\gamma^{2}-\left( 3a_{0}+\frac{1}{2}\right)\gamma+a_{0}=0. $$

By applying bilinear transformation [33, 34], i.e., γ = (ω + 1)/(ω − 1), one gets

$$ 2\omega^{2}+2\omega-8a_{0}=0. $$

From Routh stability criterion [33, 34], the 0-stable condition is a0 < 0. The general four-instant ZeaD formula (5) is obtained. The proof is thus completed.

Appendix: 3. Proof of five-instant ZeaD formula group

When m = 5, five-instant ZeaD formula can be presented as

$$\dot{x}_{k}=a_{4}\frac{x_{k+1}}{\tau}+a_{3}\frac{x_{k}}{\tau}+a_{2}\frac{x_{k-1}}{\tau}+a_{1}\frac{x_{k-2}}{\tau}+a_{0}\frac{x_{k-3}}{\tau}+O(\tau^{p}). $$

According to (2), if p = 4, one has

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} a_{4}+a_{3}+a_{2}+a_{1}+a_{0}=0,\\ a_{4}-a_{2}-2a_{1}-3a_{0}=1,\\ a_{4}+a_{2}+4a_{1}+9a_{0}=0,\\ a_{4}-a_{2}-8a_{1}-27a_{0}=0,\\ a_{4}+a_{2}+16a_{1}+81a_{0}=0, \end{array} \right. \end{array} $$

i.e.,

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} a_{4}=1/4,\\ a_{3}=5/6,\\ a_{2}=-3/2,\\ a_{1}=1/2,\\ a_{0}=-1/12. \end{array} \right. \end{array} $$

In this case, the characteristic equation is

$$ P_{4}(\gamma)=\frac{1}{4}\gamma^{4}+\frac{5}{6}\gamma^{3}-\frac{3}{2}\gamma^{2}+\frac{1}{2}\gamma-\frac{1}{12}=0. $$

By applying bilinear transformation [33, 34], i.e., γ = (ω + 1)/(ω − 1), one gets the transformed characteristic equation

$$ 2\omega^{3}+4\omega^{2}+\frac{2}{3}\omega-\frac{8}{3}=0. $$

Because there is a negative coefficient − 8/3, the synthesized formula is not 0-stable [33, 34]. That is, five-instant ZeaD formula cannot have O(τ4) precision.

Meanwhile, if p = 3, one has

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} a_{4}+a_{3}+a_{2}+a_{1}+a_{0}=0,\\ a_{4}-a_{2}-2a_{1}-3a_{0}=1,\\ a_{4}+a_{2}+4a_{1}+9a_{0}=0,\\ a_{4}-a_{2}-8a_{1}-27a_{0}=0, \end{array} \right. \end{array} $$

i.e.,

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} a_{4}=a_{0}+1/3,\\ a_{3}=-4a_{0}+1/2,\\ a_{2}=6a_{0}-1,\\ a_{1}=-4a_{0}+1/6. \end{array} \right. \end{array} $$

In this case, the characteristic equation is

$$ P_{4}(\gamma)=\left( a_{0}+\frac{1}{3}\right)\gamma^{4}-\left( 4a_{0}-\frac{1}{2}\right)\gamma^{3}+\left( 6a_{0}-1\right)\gamma^{2}-\left( 4a_{0}-\frac{1}{6}\right)\gamma+a_{0}=0. $$

By applying bilinear transformation [33, 34], i.e., γ = (ω + 1)/(ω − 1), one gets

$$ 2\omega^{3}+4\omega^{2}+\frac{2}{3}\omega+16a_{0}-\frac{4}{3}=0. $$

From Routh stability criterion [33, 34], the 0-stable condition is 1/12 < a0 < 1/6. The general five-instant ZeaD formula (6) is obtained. The proof is thus completed.

Appendix: 4. Proof of six-instant ZeaD formula group

When m = 6, six-instant ZeaD formula can be presented as

$$\dot{x}_{k}=a_{5}\frac{x_{k+1}}{\tau}+a_{4}\frac{x_{k}}{\tau}+a_{3}\frac{x_{k-1}}{\tau}+a_{2}\frac{x_{k-2}}{\tau}+a_{1}\frac{x_{k-3}}{\tau}+a_{0}\frac{x_{k-4}}{\tau}+O(\tau^{p}). $$

According to (2), if p = 4, one has

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} a_{5}+a_{4}+a_{3}+a_{2}+a_{1}+a_{0}=0,\\ a_{5}-a_{3}-2a_{2}-3a_{1}-4a_{0}=1,\\ a_{5}+a_{3}+4a_{2}+9a_{1}+16a_{0}=0,\\ a_{5}-a_{3}-8a_{2}-27a_{1}-64a_{0}=0,\\ a_{5}+a_{3}+16a_{2}+81a_{1}+256a_{0}=0, \end{array} \right. \end{array} $$

i.e.,

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} a_{5}=-a_{0}+1/4,\\ a_{4}=5a_{0}+5/6,\\ a_{3}=-10a_{0}-3/2,\\ a_{2}=10a_{0}+1/2,\\ a_{1}=-5a_{0}-1/12. \end{array} \right. \end{array} $$

In this case, the characteristic equation is

$$ \begin{array}{llll} P_{5}(\gamma)=&\left( -a_{0}+\frac{1}{4}\right)\gamma^{5}+\left( 5a_{0}+\frac{5}{6}\right)\gamma^{4}-\left( 10a_{0}+\frac{3}{2}\right)\gamma^{3}\\ &+\left( 10a_{0}+\frac{1}{2}\right)\gamma^{2}-\left( 5a_{0}+\frac{1}{12}\right)\gamma+a_{0}=0. \end{array} $$

By applying bilinear transformation [33, 34], i.e., γ = (ω + 1)/(ω − 1), one gets transformed characteristic equation

$$ 2\omega^{4}+6\omega^{3}+\frac{14}{3}\omega^{2}-2\omega-32a_{0}-\frac{8}{3}=0. $$

Because there is a negative coefficient − 2, the synthesized formula is not 0-stable [33, 34]. That is, six-instant ZeaD formula cannot have O(τ4) precision.

Meanwhile, if p = 3, one has

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} a_{5}+a_{4}+a_{3}+a_{2}+a_{1}+a_{0}=0,\\ a_{5}-a_{3}-2a_{2}-3a_{1}-4a_{0}=1,\\ a_{5}+a_{3}+4a_{2}+9a_{1}+16a_{0}=0,\\ a_{5}-a_{3}-8a_{2}-27a_{1}-64a_{0}=0, \end{array} \right. \end{array} $$

i.e.,

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} a_{5}=a_{1}+4a_{0}+1/3,\\ a_{4}=-4a_{1}-15a_{0}+1/2,\\ a_{3}=6a_{1}+20a_{0}-1,\\ a_{2}=-4a_{1}-10a_{0}+1/6. \end{array} \right. \end{array} $$

In this case, the characteristic equation is

$$ \begin{array}{llll} P_{5}(\gamma)=&\left( a_{1}+4a_{0}+\frac{1}{3}\right)\gamma^{5}-\left( 4a_{1}+15a_{0}-\frac{1}{2}\right)\gamma^{4}+(6a_{1}+20a_{0}-1)\gamma^{3}\\ &-\left( 4a_{1}+10a_{0}-\frac{1}{6}\right)\gamma^{2}+a_{1}\gamma+a_{0}=0. \end{array} $$

By applying bilinear transformation [33, 34], i.e., γ = (ω + 1)/(ω − 1), one gets

$$ 2\omega^{4}+6\omega^{3}+\frac{14}{3}\omega^{2}+\left( 16a_{1}+80a_{0}-\frac{2}{3}\right)\omega +16a_{1}+48a_{0}-\frac{4}{3}=0. $$

From Routh stability criterion [33, 34], the 0-stable condition is presented as the group form of

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} 12a_{1}+36a_{0}-1>0,\\ 24a_{1}+120a_{0}-1>0,\\ 12a_{1}+60a_{0}-11<0,\\ 36{a_{1}^{2}}+360a_{1}a_{0}+900{a_{0}^{2}}+6a_{1}-51a_{0}-2<0. \end{array} \right. \end{array} $$

By analyzing and plotting, the condition can be reduced to

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} 12a_{1}+36a_{0}-1>0,\\ 36{a_{1}^{2}}+360a_{1}a_{0}+900{a_{0}^{2}}+6a_{1}-51a_{0}-2<0. \end{array} \right. \end{array} $$

The general six-instant ZeaD formula (7) is obtained. The proof is thus completed.

Appendix: 5. Proof of seven-instant ZeaD formula group

When m = 7, seven-instant ZeaD formula can be presented as

$$ \dot{x}_{k}=a_{6}\frac{x_{k+1}}{\tau}+a_{5}\frac{x_{k}}{\tau}+a_{4}\frac{x_{k-1}}{\tau}+a_{3}\frac{x_{k-2}}{\tau}+a_{2}\frac{x_{k-3}}{\tau}+a_{1}\frac{x_{k-4}}{\tau}+a_{0}\frac{x_{k-5}}{\tau}+O(\tau^{p}). $$

According to (2), if p = 5, one has

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} a_{6}+a_{5}+a_{4}+a_{3}+a_{2}+a_{1}+a_{0}=0,\\ a_{6}-a_{4}-2a_{3}-3a_{2}-4a_{1}-5a_{0}=1,\\ a_{6}+a_{4}+4a_{3}+9a_{2}+16a_{1}+25a_{0}=0,\\ a_{6}-a_{4}-8a_{3}-27a_{2}-64a_{1}-125a_{0}=0,\\ a_{6}+a_{4}+16a_{3}+81a_{2}+256a_{1}+625a_{0}=0,\\ a_{6}-a_{4}-32a_{3}-243a_{2}-1024a_{1}-3125a_{0}=0, \end{array} \right. \end{array} $$

i.e.,

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} a_{6}=a_{0}+1/5,\\ a_{5}=-6a_{0}+13/12,\\ a_{4}=15a_{0}-2,\\ a_{3}=-20a_{0}+1,\\ a_{2}=15a_{0}-1/3,\\ a_{1}=-6a_{0}+1/20. \end{array} \right. \end{array} $$

In this case, the characteristic equation is

$$ \begin{array}{llll} P_{6}(\gamma)=&\left( a_{0}+\frac{1}{5}\right)\gamma^{6}-\left( 6a_{0}-\frac{13}{12}\right)\gamma^{5}+\left( 15a_{0}-2\right)\gamma^{4}-\left( 20a_{0}-1\right)\gamma^{3}\\ &+\left( 15a_{0}-\frac{1}{3}\right)\gamma^{2}-\left( 6a_{0}-\frac{1}{20}\right)\gamma+a_{0}=0. \end{array} $$

By applying bilinear transformation [33, 34], i.e., γ = (ω + 1)/(ω − 1), one gets transformed characteristic equation

$$ 2\omega^{5}+8\omega^{4}+\frac{32}{3}\omega^{3}+\frac{8}{3}\omega^{2}-\frac{94}{15}\omega+64a_{0}-\frac{64}{15}=0. $$

Because there is a negative coefficient − 94/15, the synthesized formula is not 0-stable [33, 34]. That is, seven-instant ZeaD formula cannot have O(τ5) precision.

Meanwhile, if p = 4, one has

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} a_{6}+a_{5}+a_{4}+a_{3}+a_{2}+a_{1}+a_{0}=0,\\ a_{6}-a_{4}-2a_{3}-3a_{2}-4a_{1}-5a_{0}=1,\\ a_{6}+a_{4}+4a_{3}+9a_{2}+16a_{1}+25a_{0}=0,\\ a_{6}-a_{4}-8a_{3}-27a_{2}-64a_{1}-125a_{0}=0,\\ a_{6}+a_{4}+16a_{3}+81a_{2}+256a_{1}+625a_{0}=0, \end{array} \right. \end{array} $$

i.e.,

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} a_{6}=-a_{1}-5a_{0}+1/4,\\ a_{5}=5a_{1}+24a_{0}+5/6,\\ a_{4}=-10a_{1}-45a_{0}-3/2,\\ a_{3}=10a_{1}+40a_{0}+1/2,\\ a_{2}=-5a_{1}-15a_{0}-1/12. \end{array} \right. \end{array} $$

In this case, the characteristic equation is

$$ \begin{array}{llll} P_{6}(\gamma)=&\left( -a_{1}-5a_{0}+\frac{1}{4}\right)\gamma^{6}+\left( 5a_{1}+24a_{0}+\frac{5}{6}\right)\gamma^{5}\\ &-\left( 10a_{1}+45a_{0}+\frac{3}{2}\right)\gamma^{4}+\left( 10a_{1}+40a_{0}+\frac{1}{2}\right)\gamma^{3}\\ &-\left( 5a_{1}+15a_{0}+\frac{1}{12}\right)\gamma^{2}+a_{1}\gamma+a_{0}=0. \end{array} $$

By applying bilinear transformation [33, 34], i.e., γ = (ω + 1)/(ω − 1), one gets

$$ 2\omega^{5}+8\omega^{4}+\frac{32}{3}\omega^{3}+\frac{8}{3}\omega^{2}-\left( 32a_{1}+192a_{0}+\frac{14}{3}\right)\omega-\left( 32a_{1}+128a_{0}+\frac{8}{3}\right)=0. $$

From Routh stability criterion [33, 34], the 0-stable condition is in the group form as follows:

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} 48a_{1}+288a_{0}+7<0,\\ 12a_{1}+48a_{0}+1<0,\\ 36a_{1}+240a_{0}+11>0,\\ 432{a_{1}^{2}}+5760a_{1}a_{0}+19200{a_{0}^{2}}-96a_{1}+160a_{0}-3<0. \end{array} \right. \end{array} $$

By analyzing and plotting, the condition can be reduced to

$$ \begin{array}{@{}rcl@{}} \left\{ \begin{array}{l} 12a_{1}+48a_{0}+1<0,\\ 432{a_{1}^{2}}+5760a_{1}a_{0}+19200{a_{0}^{2}}-96a_{1}+160a_{0}-3<0. \end{array} \right. \end{array} $$

The general seven-instant ZeaD formula (8) is obtained. The proof is thus completed.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, M., Zhang, Y. & Hu, H. Relationship between time-instant number and precision of ZeaD formulas with proofs. Numer Algor 88, 883–902 (2021). https://doi.org/10.1007/s11075-020-01061-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11075-020-01061-x

Keywords

Navigation