Discrete-time noise-tolerant Z-type model for online solving nonlinear time-varying equations in the presence of noises

Abstract Nonlinear time-varying equation problems (NTVEPs), a core mathematical problem in engineering applications and scientific computing fields, have been widely researched in recent years. In this paper, the zeroing-dynamic design formula and continuous-time Z-type model are revisited for solving NTVEPs. Then, a modified Z-type design formula is developed to address NTVEPs in the presence of noises. Specifically, a novel class of discrete-time noise-tolerant Z-type model with ψ τ ( χ ( τ ) , τ ) known (DTNTZTM-K) and discrete-time noise-tolerant Z-type model with ψ τ ( χ ( τ ) , τ ) unknown (DTNTZTM-U) models are first proposed and investigated for online solving NTVEPs with different measurement noises. Furthermore, a general-type DTNTZTM-K and DTNTZTM-U models (termed as GDTNTZTM-K and GDTNTZTM-U models) with different activation function are proposed to verify the robustness and superiority. In addition, theoretical analyses demonstrate that the presented DTNTZTM-K and DTNTZTM-U models are 0-stable, consistent and convergent. Besides, it further indicates that different activation functions can be utilized to accelerate the convergent speed of a class of general discrete-time noise-tolerant Z-type models, which demonstrates their high efficiency and robustness. Ultimately, numerical results show that the efficacy and superiority of the proposed DTNTZTM-K, DTNTZTM-U, GDTNTZTM-K and GDTNTZTM-U models for noise-polluted NTVEPs compared with classical methods.

[1]  Jun Wang,et al.  A recurrent neural network for solving Sylvester equation with time-varying coefficients , 2002, IEEE Trans. Neural Networks.

[2]  Yunong Zhang,et al.  Continuous and discrete time Zhang dynamics for time-varying 4th root finding , 2010, Numerical Algorithms.

[3]  Zhongbo Sun,et al.  A new trust region–sequential quadratic programming approach for nonlinear systems based on nonlinear model predictive control , 2019 .

[4]  Yantao Tian,et al.  A novel projected Fletcher‐Reeves conjugate gradient approach for finite‐time optimal robust controller of linear constraints optimization problem: Application to bipedal walking robots , 2018 .

[5]  Shuai Li,et al.  Neural Dynamics for Cooperative Control of Redundant Robot Manipulators , 2018, IEEE Transactions on Industrial Informatics.

[6]  Shuzhi Sam Ge,et al.  Design and analysis of a general recurrent neural network model for time-varying matrix inversion , 2005, IEEE Transactions on Neural Networks.

[7]  M. Frontini,et al.  Hermite interpolation and a new iterative method¶for the computation of the roots¶of non-linear equations , 2003 .

[8]  Binghuang Cai,et al.  From Zhang Neural Network to Newton Iteration for Matrix Inversion , 2009, IEEE Transactions on Circuits and Systems I: Regular Papers.

[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]  Bolin Liao,et al.  Noise-Resistant Discrete-Time Neural Dynamics for Computing Time-Dependent Lyapunov Equation , 2018, IEEE Access.

[11]  Chenguang Yang,et al.  New Noise-Tolerant Neural Algorithms for Future Dynamic Nonlinear Optimization With Estimation on Hessian Matrix Inversion , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[12]  Karim Djouani,et al.  Toward Lower Limbs Functional Rehabilitation Through a Knee-Joint Exoskeleton , 2017, IEEE Transactions on Control Systems Technology.

[13]  B. Ghanbari A new model for investigating the transmission of infectious diseases in a prey‐predator system using a non‐singular fractional derivative , 2021, Mathematical Methods in the Applied Sciences.

[14]  Yunong Zhang,et al.  Solution of nonlinear equations by continuous- and discrete-time Zhang dynamics and more importantly their links to Newton iteration , 2009, 2009 7th International Conference on Information, Communications and Signal Processing (ICICS).

[15]  Rachel W Jackson,et al.  Human-in-the-loop optimization of exoskeleton assistance during walking , 2017, Science.

[16]  Behzad Ghanbari,et al.  Chaotic behaviors of the prevalence of an infectious disease in a prey and predator system using fractional derivatives , 2021, Mathematical Methods in the Applied Sciences.

[17]  B. Ghanbari,et al.  Mathematical and numerical analysis of a three‐species predator‐prey model with herd behavior and time fractional‐order derivative , 2019, Mathematical Methods in the Applied Sciences.

[18]  Claude Brezinski,et al.  A Classification of Quasi-Newton Methods , 2003, Numerical Algorithms.

[19]  Shuai Li,et al.  RNN Models for Dynamic Matrix Inversion: A Control-Theoretical Perspective , 2018, IEEE Transactions on Industrial Informatics.

[20]  Xiang Chen,et al.  A Visual Distance Approach for Multicamera Deployment With Coverage Optimization , 2018, IEEE/ASME Transactions on Mechatronics.

[21]  Long Jin,et al.  Continuous and discrete Zhang dynamics for real-time varying nonlinear optimization , 2015, Numerical Algorithms.

[22]  Shuai Li,et al.  Modified ZNN for Time-Varying Quadratic Programming With Inherent Tolerance to Noises and Its Application to Kinematic Redundancy Resolution of Robot Manipulators , 2016, IEEE Transactions on Industrial Electronics.

[23]  Morten Hovd,et al.  Constrained Control of Uncertain, Time-varying Linear Discrete-Time Systems Subject to Bounded Disturbances , 2015, IEEE Transactions on Automatic Control.

[24]  A. Atangana,et al.  Some new edge detecting techniques based on fractional derivatives with non-local and non-singular kernels , 2020, Advances in Difference Equations.

[25]  Peiyao Shen,et al.  Complete and Time-Optimal Path-Constrained Trajectory Planning With Torque and Velocity Constraints: Theory and Applications , 2018, IEEE/ASME Transactions on Mechatronics.

[26]  Carver Mead,et al.  Analog VLSI and neural systems , 1989 .

[27]  Jian Li,et al.  Noise-tolerant Z-type neural dynamics for online solving time-varying inverse square root problems: A control-based approach , 2020, Neurocomputing.

[28]  J. R. Sharma,et al.  A composite third order Newton-Steffensen method for solving nonlinear equations , 2005, Appl. Math. Comput..

[29]  Feng Li,et al.  Different modified zeroing neural dynamics with inherent tolerance to noises for time-varying reciprocal problems: A control-theoretic approach , 2019, Neurocomputing.

[30]  Changbum Chun,et al.  Construction of Newton-like iteration methods for solving nonlinear equations , 2006, Numerische Mathematik.

[31]  Nenad Ujevic,et al.  A method for solving nonlinear equations , 2006, Appl. Math. Comput..

[32]  ZHONGBO SUN,et al.  Two DTZNN Models of O(τ4) Pattern for Online Solving Dynamic System of Linear Equations: Application to Manipulator Motion Generation , 2020, IEEE Access.

[33]  Shuai Li,et al.  Zeroing neural networks: A survey , 2017, Neurocomputing.

[34]  Abderrazak Nabti,et al.  Global stability analysis of a fractional SVEIR epidemic model , 2021, Mathematical Methods in the Applied Sciences.

[35]  Long Jin,et al.  Noise-suppressing zeroing neural network for online solving time-varying nonlinear optimization problem: a control-based approach , 2019, Neural Computing and Applications.

[36]  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.

[37]  Shuai Li,et al.  Noise-Tolerant ZNN Models for Solving Time-Varying Zero-Finding Problems: A Control-Theoretic Approach , 2017, IEEE Transactions on Automatic Control.

[38]  Shuai Li,et al.  On Generalized RMP Scheme for Redundant Robot Manipulators Aided With Dynamic Neural Networks and Nonconvex Bound Constraints , 2019, IEEE Transactions on Industrial Informatics.

[39]  Yantao Tian,et al.  A superlinear convergence feasible sequential quadratic programming algorithm for bipedal dynamic walking robot via discrete mechanics and optimal control , 2016 .

[40]  Zhen Li,et al.  Discrete-time ZD, GD and NI for solving nonlinear time-varying equations , 2012, Numerical Algorithms.

[41]  Yunong Zhang,et al.  Time-series Gaussian Process Regression Based on Toeplitz Computation of O(N2) Operations and O(N)-level Storage , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[42]  Zhongbo Sun,et al.  Five-step discrete-time noise-tolerant zeroing neural network model for time-varying matrix inversion with application to manipulator motion generation , 2021, Eng. Appl. Artif. Intell..

[43]  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.