Any ZeaD Formula of Six Instants Having No Quartic or Higher Precision with Proof

In recent years, Zhang et al. discretization (ZeaD) as a new class of time-discretization methods has been proposed, named and applied by Zhang et al. Note that ZeaD formulas can accurately discretize Zhang neural networks $(\mathrm {i}.\mathrm {e}.$, ZNN, or say, Zhang dynamics) models as well as ordinary differential equation systems. In previous work, various ZeaD formulas have been presented and unified, including Euler forward formula as 2-instant ZeaD formula that is convergent with a truncation error being proportional to the first power of sampling period and Taylor-type discretization formula as 4-instant ZeaD formula that is convergent with a truncation error being proportional to the second power of sampling period. During our pursuit of ZeaD formulas that are convergent with a higher precision, we discover that there exists no 6-instant ZeaD formula that is convergent with a quartic (ie, biquadratic, of degree 4) or higher precision. The truncation error of any 6-instant ZeaD formula is proportional to the third power of sampling period or bigger. The contributions are theoretically proved in this paper as well.

[1]  R. Ohba,et al.  New finite difference formulas for numerical differentiation , 2000 .

[2]  Bo Xu,et al.  Infinitely many periodic solutions of ordinary differential equations , 2014, Appl. Math. Lett..

[3]  Desmond J. Higham,et al.  Numerical Methods for Ordinary Differential Equations - Initial Value Problems , 2010, Springer undergraduate mathematics series.

[4]  Yunong Zhang,et al.  ZFD formula 4IgSFD_Y applied to future minimization , 2017 .

[5]  Yunong Zhang,et al.  New formula 4IgSFD_L of Zhang finite difference for 1st-order derivative approximation with numerical experiment verification , 2016, 2016 5th International Conference on Computer Science and Network Technology (ICCSNT).

[6]  Yunong Zhang,et al.  Two New Discrete-Time Neurodynamic Algorithms Applied to Online Future Matrix Inversion With Nonsingular or Sometimes-Singular Coefficient , 2019, IEEE Transactions on Cybernetics.

[7]  Yunong Zhang,et al.  New formula of 4-instant g-square finite difference (4IgSFD) applied to time-variant matrix inversion , 2015, The 27th Chinese Control and Decision Conference (2015 CCDC).

[8]  Jean-Pierre Tignol,et al.  Galois' theory of algebraic equations , 1988 .

[9]  Yunong Zhang,et al.  Zhang neural network for online solution of time-varying convex quadratic program subject to time-varying linear-equality constraints , 2009 .

[10]  Zhiming Wu,et al.  Electrically tunable magnetic configuration on vacancy-doped GaSe monolayer , 2018 .

[11]  Long Jin,et al.  Infinitely many Zhang functions resulting in various ZNN models for time-varying matrix inversion with link to Drazin inverse , 2015, Inf. Process. Lett..

[12]  I. R. Khan,et al.  Taylor series based finite difference approximations of higher-degree derivatives , 2003 .

[13]  Long Jin,et al.  Discrete-time Zhang neural network of O(τ3) pattern for time-varying matrix pseudoinversion with application to manipulator motion generation , 2014, Neurocomputing.

[14]  Katsuhiko Ogata,et al.  Modern Control Engineering , 1970 .

[15]  Yunong Zhang,et al.  New Discrete-Time ZNN Models for Least-Squares Solution of Dynamic Linear Equation System With Time-Varying Rank-Deficient Coefficient , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[16]  I. R. Khan,et al.  Closed-form expressions for the finite approximations of first and higher derivatives based on Taylor series , 1999 .

[17]  Yong Wang,et al.  Parameter Estimation of Hybrid Linear Frequency Modulation-Sinusoidal Frequency Modulation Signal , 2017, IEEE Signal Processing Letters.

[18]  Dongsheng Guo,et al.  Novel Discrete-Time Zhang Neural Network for Time-Varying Matrix Inversion , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[19]  Long Jin,et al.  Discrete-Time Zhang Neural Network for Online Time-Varying Nonlinear Optimization With Application to Manipulator Motion Generation , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[20]  Dong-Juan Li,et al.  Adaptive Control via Neural Output Feedback for a Class of Nonlinear Discrete-Time Systems in a Nested Interconnected Form , 2018, IEEE Transactions on Cybernetics.

[21]  P. P. Vaidyanathan,et al.  Arbitrarily Shaped Periods in Multidimensional Discrete Time Periodicity , 2015, IEEE Signal Processing Letters.

[22]  Wei Jiang,et al.  Broadband aberration-free focusing reflector for acoustic waves , 2017 .

[23]  Yunong Zhang,et al.  A dual neural network for convex quadratic programming subject to linear equality and inequality constraints , 2002 .

[24]  Yunong Zhang,et al.  Proposing and Validation of a New Four-Point Finite-Difference Formula With Manipulator Application , 2018, IEEE Transactions on Industrial Informatics.

[25]  Shuai Lu,et al.  Numerical differentiation from a viewpoint of regularization theory , 2006, Math. Comput..

[26]  Alan V. Oppenheim,et al.  Discrete-time Signal Processing. Vol.2 , 2001 .

[27]  James Lam,et al.  Robust filtering for discrete-time Markovian jump delay systems , 2004, IEEE Signal Processing Letters.

[28]  Long Jin,et al.  Taylor $O(h^{3})$ Discretization of ZNN Models for Dynamic Equality-Constrained Quadratic Programming With Application to Manipulators , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[29]  Jianping Li General explicit difference formulas for numerical differentiation , 2005 .

[30]  Yunong Zhang,et al.  New formula ZD4IgS_Q applied to solving future nonlinear systems of equations with abundant numerical experiment verification , 2017, 2017 17th International Conference on Control, Automation and Systems (ICCAS).

[31]  Jianxiong Cao,et al.  A high order numerical scheme for variable order fractional ordinary differential equation , 2016, Appl. Math. Lett..

[32]  Dongsheng Guo,et al.  Theoretical analysis, numerical verification and geometrical representation of new three-step DTZD algorithm for time-varying nonlinear equations solving , 2016, Neurocomputing.

[33]  Masaaki Nagahara,et al.  Discrete Signal Reconstruction by Sum of Absolute Values , 2015, IEEE Signal Processing Letters.

[34]  Dongsheng Guo,et al.  Design and analysis of two discrete-time ZD algorithms for time-varying nonlinear minimization , 2017, Numerical Algorithms.

[35]  Long Jin,et al.  Neural network-based discrete-time Z-type model of high accuracy in noisy environments for solving dynamic system of linear equations , 2016, Neural Computing and Applications.

[36]  Zhongdi Cen,et al.  A remainder formula of numerical differentiation for the generalized Lagrange interpolation , 2009 .

[37]  Yang Shi,et al.  General four-step discrete-time zeroing and derivative dynamics applied to time-varying nonlinear optimization , 2019, J. Comput. Appl. Math..