A noise-tolerant fast convergence ZNN for dynamic matrix inversion

References

• C. Aouiti and F. Miaadi, A new fixed-time stabilization approach for neural networks with time-varying delays. Neural Comput. Appl. 32 (2020), pp. 3295–3309.
   Web of Science ®Google Scholar
• J. Chen and Y. Zhang, Discrete-time ZND models solving ALRMPC via eight-instant general and other formulas of ZeaD. IEEE Access 7 (2019), pp. 125909–125918.
   Web of Science ®Google Scholar
• J. Chen and Y. Zhang, Continuous and discrete zeroing neural dynamics handling future unknown-transpose matrix inequality as well as scalar inequality of linear class. Numer. Algorithms 83 (2020), pp. 529–547.
   Web of Science ®Google Scholar
• J. Chen and Y. Zhang, Online singular value decomposition of time-varying matrix via zeroing neural dynamics. Neurocomputing 383 (2020), pp. 314–323.
   Web of Science ®Google Scholar
• J. Gong and J. Jin, A better robustness and fast convergence zeroing neural network for solving dynamic nonlinear equations. Neural Comput. Appl. (2021), doi:https://doi.org/10.1007/s00521-020-05617-9.
   Web of Science ®Google Scholar
• J. Guo, B. Qiu, J. Chen, and Y. Zhang, Solving Future different-layer nonlinear and linear equation system using new eight-node DZNN model. IEEE Trans. Ind. Inf. 16 (4) (2020), pp. 2280–2289.
   Web of Science ®Google Scholar
• D. Guo and Y. Zhang, Zhang neural network, Getz-Marsden dynamic system, and discrete-time algorithms for time-varying matrix inversion with application to robots’ kinematic control. Neurocomputing 97 (2012), pp. 22–32.
   Web of Science ®Google Scholar
• D. Guo and Y. Zhang, Zhang neural network, Getz–Marsden dynamic system, and discrete-time algorithms for time-varying matrix inversion with application to robots’ kinematic control. Neurocomputing 97 (2012), pp. 22–32.
   Web of Science ®Google Scholar
• D. Guo and Y. Zhang, Acceleration-level inequality-based MAN scheme for obstacle avoidance of redundant robot manipulators. IEEE Trans. Ind. Electron. 61(12) (2014), pp. 6903–6914.
   Web of Science ®Google Scholar
• F.K. Haghani and F. Soleymani, An improved Schulz-type iterative method for matrix inversion with application. Trans. Inst. Meas. Control 36(8) (2014), pp. 983–991.
   Web of Science ®Google Scholar
• B. Hu, H. Zhao, Y. Yang, B. Zhou, and A. Noel, Multiple faces tracking using feature fusion and neural network in video. Intell. Autom. Soft Comput. 26(6) (2020), pp. 1549–1560.
   Web of Science ®Google Scholar
• J. Ji and X. Chen, A new method for computing Moore–Penrose inverse through Gauss–Jordan elimination. Appl. Math. Comput. 245 (2014), pp. 271–278.
   Web of Science ®Google Scholar
• J. Jin, A robust zeroing neural network for solving dynamic nonlinear equations and its application to kinematic control of mobile manipulator. Complex Intell. Syst. (2020), doi:https://doi.org/10.1007/s40747-020-00178-9.
   Web of Science ®Google Scholar
• J. Jin, An improved finite time convergence Recurrent neural network with application to time-varying Linear complex matrix equation solution. Neural Process. Lett. (2021), doi:https://doi.org/10.1007/s11063-021-10426-9.
   Web of Science ®Google Scholar
• J. Jin and J. Gong, An interference-tolerant fast convergence zeroing neural network for dynamic matrix inversion and its application to mobile manipulator path tracking. Alexandria Eng. J. 60 (2021), pp. 659–669.
   Web of Science ®Google Scholar
• L. Jin, S. Li, B. Hu, M. Liu, and J. Yu, Noise-suppressing neural algorithm for solving time-varying system of linear equations: A control-based approach. IEEE Trans. Ind. Inf. 15(1) (2019), pp. 236–246.
   Web of Science ®Google Scholar
• J. Jin, L. Xiao, M. Lu, and J. Li, Design and analysis of Two FTRNN models with application to time-varying Sylvester equation. IEEE. Access 7 (2019), pp. 58945–58950.
   Web of Science ®Google Scholar
• L. Jin and Y. Zhang, G2-type SRMPC scheme for synchronous manipulation of two redundant robot arms. IEEE Trans. Cybern. 45(2) (2014), pp. 153–164.
   PubMed Web of Science ®Google Scholar
• L. Jin, Y. Zhang, and S. Li, Integration-enhanced Zhang neural network for real-time-varying matrix inversion in the presence of various kinds of noises. IEEE Trans. Neural Netw. Learn. Syst. 27(12) (2016), pp. 2615–2627.
   PubMed Web of Science ®Google Scholar
• L. Jin, Y. Zhang, S. Li, and Y. Zhang, Noise-tolerant ZNN models for solving time-varying zero-finding problems: A control-theoretic approach. IEEE Trans. Autom. Control 62(2) (2017), pp. 992–997.
   Web of Science ®Google Scholar
• J. Jin, L. Zhao, M. Li, F. Yu, and Z. Xi, Improved zeroing neural networks for finite time solving nonlinear equations. Neural Comput. Appl. 32 (2020), pp. 4151–4160.
   Web of Science ®Google Scholar
• S. Li, S. Chen, and B. Liu, Accelerating a recurrent neural network to finite-time convergence for solving time-varying Sylvester equation by using a sign-bi-power activation function. Neural Process. Lett. 37(2) (2013), pp. 189–205.
   Web of Science ®Google Scholar
• W. Li, Design and analysis of a novel finite-time convergent and noise-tolerant recurrent neural network for time-variant matrix inversion. IEEE Trans. Syst., Man, Cybern. Syst. 2020,https://doi.org/ doi:10.1109/TSMC.2018.2853598.
   Web of Science ®Google Scholar
• S. Li, J. He, Y. Li, and M.U. Rafique, Distributed recurrent neural networks for cooperative control of manipulators: A game-theoretic perspective. IEEE Trans. Neural Netw. Learn. Syst. 28(2) (2017), pp. 415–426.
   PubMed Web of Science ®Google Scholar
• X. Lv, L. Xiao, Z. Tan, Z. Yang, and J. Yuan, Improved gradient neural networks for solving Moore–Penrose inverse of full-rank matrix. Neural Process. Lett. (2019), doi:https://doi.org/10.1007/s11063-019-09983-x.
   PubMed Web of Science ®Google Scholar
• X. Ma, Y. Lu, Y. Lu, Z. Pei, and J. Liu, Biomedical event extraction using a new error detection learning approach based on neural network. Comput. Mater. Con. 63(2) (2020), pp. 923–941.
   Web of Science ®Google Scholar
• L. Pan, C. Li, S. Pouyanfar, R. Chen, and Y. Zhou, A novel combinational convolutional neural network for automatic food-ingredient classification. Comput. Mater. Con. 62(2) (2020), pp. 731–746.
   Web of Science ®Google Scholar
• V. Pan, F. Soleymani, and L. Zhao, An efficient computation of generalized inverse of a matrix. Appl. Math. Comput. 316 (2018), pp. 89–101.
   Web of Science ®Google Scholar
• A. Polyakov, Nonlinear feedback design for fixed-time stabilization of linear control systems. IEEE Trans. Autom. Control 57(8) (2012), pp. 2106–2110.
   Web of Science ®Google Scholar
• Z. Quan, Y. Zakharov, and J. Liu, DCD-based simplified matrix inversion for MIMO-OFDM. IEEE Int. Symposium Circuits Syst. (2011), pp. 2389–2392.
   Google Scholar
• Y. Shen, P. Miao, Y. Huang, and Y. Shen, Finite-time stability and its application for solving time-varying Sylvester equation by recurrent neural network. Neural Process. Lett. 42(3) (2015), pp. 763–784.
   Web of Science ®Google Scholar
• P.S. Stanimirovic, M.D. Petkovic, D. Gerontitis, Gradient neural network with nonlinear activation for computing inner inverses and the Drazin inverse. Neural Process. Lett. 48(2018)，pp. 109–133.
   Web of Science ®Google Scholar
• C. Tang, C. Liu, L. Yuan, and Z. Xing, High precision low complexity matrix inversion based on Newton iteration for data detection in the massive MIMO. IEEE Commun. Lett. 20(3) (2016), pp. 490–493.
   Web of Science ®Google Scholar
• J. Wang, Recurrent neural networks for computing pseudo-inverses of rank-deficient matrices. SIAM J. Sci. Comput. 18(5) (1997), pp. 1479–1493.
   Web of Science ®Google Scholar
• Y. Wang and J. Bo, Fast algorithm for matrix inversion in generalized predictive control, Second International Conference on Mechanic Automation and Control Engineering, Hohhot, 2011, pp. 7367–7371.
   Google Scholar
• Y. Wang and H. Lei, Sphere decoding for MIMO systems with Newton iterative matrix inversion. IEEE Commun. Lett. 17(2) (2013), pp. 389–392.
   Web of Science ®Google Scholar
• X. Wang, H. Ma, and P.S. Stanimirovic, Nonlinearly activated Recurrent neural network for computing the Drazin inverse. Neural Process. Lett. 46(1) (2017), pp. 195–217.
   Web of Science ®Google Scholar
• L. Xiao and B. Liao, A convergence-accelerated Zhang neural network and its solution application to Lyapunov equation. Neurocomputing 193 (2016), pp. 213–218.
   Web of Science ®Google Scholar
• L. Xiao and Y. Zhang, A new performance index for the repetitive motion of mobile manipulators. IEEE Trans. Cybern. 44(2) (2014), pp. 280–292.
   PubMed Web of Science ®Google Scholar
• L. Xiao and Y. Zhang, Solving time-varying inverse kinematics problem of wheeled mobile manipulators using Zhang neural network with exponential convergence. Nonlinear Dyn. 76(2) (2014), pp. 1543–1559.
   Web of Science ®Google Scholar
• L. Xiao, Y. Zhang, J. Dai, K. Chen, S. Yang, W. Li, B. Liao, L. Ding, and J. Li, A new noise-tolerant and predefined-time ZNN model for time-dependent matrix inversion. Neural Netw. 117 (2019), pp. 124–134.
   PubMed Web of Science ®Google Scholar
• M. Yan, B. Feng, and T. Song, On matrix inversion for LTE MIMO applications using Texas Instruments floating point DSP, IEEE 10th International Conference on Signal Processing Proceedings, Beijing, 2010, pp. 633–636.
   Google Scholar
• Y. Zhang, Revisit the analog computer and gradient-based neural system for matrix inversion, Intelligent Control 2005. Proceedings of the 2005 IEEE International Symposium on Mediterrean Conference on Control and Automation, 2005, pp. 1411–1416.
   Google Scholar
• Z. Zhang, A. Beck, and N. Magnenat-Thalmann, Human-like behavior generation based on head-arms model for tracking external targets and body parts. IEEE Trans. Cybern. 45(8) (2015), pp. 1390–1400.
   PubMed Web of Science ®Google Scholar
• Y. Zhang, K. Chen, X. Li, C. Yi, and H. Zhu, Simulink modeling and comparison of Zhang neural networks and gradient neural networks for time-varying Lyapunov equation solving, Proc. IEEE Int. Conf. Nat. Comput. 3 (2008), pp. 521–525.
   Google Scholar
• Y. Zhang and S.S. Ge, Design and analysis of a general recurrent neural network model for time-varying matrix inversion. IEEE Trans. Neural Netw. 16(6) (2005), pp. 1477–1490.
   PubMed Web of Science ®Google Scholar
• Y. Zhang, D. Guo, and Z. Li, Common nature of learning between backpropagation and Hopfield-type neural networks for generalized matrix inversion with simplified models. IEEE Trans. Neural Netw. Learn. Syst. 24(4) (2013), pp. 579–592.
   PubMed Web of Science ®Google Scholar
• Y. Zhang, Y. Shi, K. Chen, and C. Wang, Global exponential convergence and stability of gradient-based neural network for online matrix inversion. Appl. Math. Comput. 215 (2009), pp. 1301–1306.
   Web of Science ®Google Scholar
• Y. Zhang, Y. Yang, N. Tan, B. Cai, Zhang neural network solving for time-varying full-rank matrix Moore–Penrose inverse. Computing 92 (2011), pp. 97–121.
   Web of Science ®Google Scholar
• T. Zhao, K. Roeder, and H. Liu, Positive semidefinite rank-based correlation matrix estimation with application to semiparametric graph estimation. J. Comput. Graph. Stat. 23(4) (2014), pp. 895–922.
   PubMed Web of Science ®Google Scholar
• D. Zhu, Y. Sun, X. Li, and R. Qu, Massive files prefetching model based on LSTM neural network with cache transaction strategy. Comput. Mater. Con. 63(2) (2020), pp. 979–993.
   Web of Science ®Google Scholar