Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Among this study, a vary-parameter convergence-accelerated neural network (VPCANN) model is generalized to solving dynamic matrix pseudoinverse, which can achieve super exponential convergence and noise-resistant, compared to the traditional Zhang neural network (ZNN) designed for dynamic problems. Simulative experiments reveal that the neural state solutions synthesized by the VPCANN can quickly approach to the theoretical pseudoinverse. Moreover, based on three types of noise disturbance including constant noise, random noise and dynamic noise, comparisons between the VPCANN and ZNN model are also investigated, verifying noise-resistant of the VPCANN model is better than the ZNN. In addition, to show the potential application of the VPCANN in practice, the kinematic motion planning of a six-links robot manipulator is considered, further substantiating the efficacy of the VPCANN in the dynamic matrix pseudoinverse.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Guo D, Zhang Y (2014) Li-function activated znn with finite-time convergence applied to redundant-manipulator kinematic control via time-varying jacobian matrix pseudoinversion. Appl Soft Comput 24:158–168

    Article  Google Scholar 

  2. Guo D, Xu F, Yan L (2017) New pseudoinverse-based path-planning scheme with pid characteristic for redundant robot manipulators in the presence of noise. IEEE Trans Control Syst Technol 26(6):2008–2019

    Article  Google Scholar 

  3. Jin L, Li S, Wang H, Zhang Z (2018) Nonconvex projection activated zeroing neurodynamic models for time-varying matrix pseudoinversion with accelerated finite-time convergence. Appl Soft Comput 62:840–850

    Article  Google Scholar 

  4. Liu J, Chen S, Tan X, Zhang D (2007) Efficient pseudoinverse linear discriminant analysis and its nonlinear form for face recognition. Int J Pattern Recognit Artif Intell 21(08):1265–1278

    Article  Google Scholar 

  5. Wang K, Guo P, Yin Q, Luo A-L, Xin, (2016) A pseudoinverse incremental algorithm for fast training deep neural networks with application to spectra pattern recognition. In: Proceedings of 2016 International Joint Conference on Neural Networks (IJCNN), pp. 3453–3460

  6. Zhang L, Zhang D (2014) Domain adaptation extreme learning machines for drift compensation in e-nose systems. IEEE Trans Instrum Meas 64(7):1790–1801

    Article  Google Scholar 

  7. Wei Y, Cai J, Ng MK (2004) Computing moore-penrose inverses of toeplitz matrices by Newton’s iteration. Math Comput Modell 40(1–2):181–191

    Article  MathSciNet  Google Scholar 

  8. Huang F, Zhang X (2006) An improved newton iteration for the weighted Moore–Penrose inverse. Appl Math Comput 174(2):1460–1486

    MathSciNet  MATH  Google Scholar 

  9. Zhang Y, Tan N, Cai B, Chen Z (2008) Matlab simulink modeling of zhang neural network solving for time-varying pseudoinverse in comparison with gradient neural network. In: Proceedings of 2008 international symposium on intelligent information technology application, vol 1. pp 39–43

  10. Cheng L, Liu Y, Hou ZG, Tan M, Fei M (2019) A rapid spiking neural network approach with an application on hand gesture recognition. IEEE Trans Cognit Dev Syst. https://doi.org/10.1109/TCDS.2019.2918228

    Article  Google Scholar 

  11. Liao B, Zhang Y (2014) From different zfs to different znn models accelerated via li activation functions to finite-time convergence for time-varying matrix pseudoinversion. Neurocomputing 133:512–522

    Article  Google Scholar 

  12. Xiao L, Zhang Y (2014) From different zhang functions to various znn models accelerated to finite-time convergence for time-varying linear matrix equation. Neural Process Lett 39(3):309–326

    Article  Google Scholar 

  13. Li X, Yu J, Li S, Ni L (2018) A nonlinear and noise-tolerant znn model solving for time-varying linear matrix equation. Neurocomputing 317:70–78

    Article  Google Scholar 

  14. Zhang Y, Yang Y, Tan N, Cai B (2011) Zhang neural network solving for time-varying full-rank matrix Moore–Penrose inverse. Computing 92(2):97–121

    Article  MathSciNet  Google Scholar 

  15. Zhang Z, Zhang Y (2013) Design and experimentation of acceleration-level drift-free scheme aided by two recurrent neural networks. IET Control Theory Appl 7(1):25–42

    Article  MathSciNet  Google Scholar 

  16. Zhang Z, Fu T, Yan Z, Jin L, Xiao L, Sun Y, Yu Z, Li Y (2018) A varying-parameter convergent-differential neural network for solving joint-angular-drift problems of redundant robot manipulators. IEEE ASME Trans Mechatron 23(2):679–689

    Article  Google Scholar 

  17. Guo D, Zhang Y (2015) Znn for solving online time-varying linear matrix-vector inequality via equality conversion. Appl Math Comput 259:327–338

    MathSciNet  MATH  Google Scholar 

  18. Zhang Y, Li W, Guo D, Ke Z (2013) Different zhang functions leading to different znn models illustrated via time-varying matrix square roots finding. Expert Syst Appl 40(11):4393–4403

    Article  Google Scholar 

  19. Jin L, Zhang Y (2015) Discrete-time zhang neural network for online time-varying nonlinear optimization with application to manipulator motion generation. IEEE Trans Neural Netw Learn Syst 26(7):1525–1531

    Article  MathSciNet  Google Scholar 

  20. Chen D, Li S, Lin F-J, Wu Q (2019) 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

    Article  Google Scholar 

  21. Xiao L, Dai J, Lu R, Li S, Li J, Wang S (2020) Design and comprehensive analysis of a noise-tolerant znn model with limited-time convergence for time-dependent nonlinear minimization. IEEE Transa Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2020.2966294

    Article  MathSciNet  Google Scholar 

  22. Zhang Z, Zheng L, Weng J, Mao Y, Lu W, Xiao L (2018) A new varying-parameter recurrent neural-network for online solution of time-varying sylvester equation. IEEE Trans Cybern 48(11):3135–3148

    Article  Google Scholar 

  23. Zhang Z, Yan Z (2018) Hybrid-level joint-drift-free scheme of redundant robot manipulators synthesized by a varying-parameter recurrent neural network. IEEE Access 6:34967–34975

    Article  Google Scholar 

  24. Zhang Z, Li S, Zhang X (2016) Simulink comparison of varying-parameter convergent-differential neural-network and gradient neural network for solving online linear time-varying equations. In: Proceedings of 2016 world congress on intelligent control and automation (WCICA). pp 887–894

  25. Zhang Z, Kong L, Zheng L, Zhang P, Qu X, Liao B, Yu Z (2018) Robustness analysis of a power-type varying-parameter recurrent neural network for solving time-varying qm and qp problems and applications. IEEE Trans Syst Man Cybern Syst. https://doi.org/10.1109/TSMC.2018.2866843

    Article  Google Scholar 

  26. Karlik B, Olgac AV (2011) Performance analysis of various activation functions in generalized mlp architectures of neural networks. Int J Artif Intell Expert Syst 1(4):111–122

    Google Scholar 

  27. Zhang Y, Jin L, Ke Z (2012) Superior performance of using hyperbolic sine activation functions in znn illustrated via time-varying matrix square roots finding. Comput Sci Inf Syst 9(4):1603–1625

    Article  Google Scholar 

  28. Li S, Chen S, Liu B (2013) 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):189–205

    Article  Google Scholar 

  29. Shen Y, Miao P, Huang Y, Shen Y (2015) Finite-time stability and its application for solving time-varying sylvester equation by recurrent neural network. Neural Process Lett 42(3):763–784

    Article  Google Scholar 

  30. Jin L, Zhang Y, Li S (2015) 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):2615–2627

    Article  Google Scholar 

  31. Zhang Y, Chen K (2008) Comparison on zhang neural network and gradient neural network for time-varying linear matrix equation \(a\times \text{b}= \text{ c }\) solving. In: Proceedings of IEEE international conference on industrial technology (ICIT). https://doi.org/10.1109/ICIT.2008.4608579

  32. Li X, Yu J, Li S, Shao Z, Ni L (2019) A non-linear and noise-tolerant znn model and its application to static and time-varying matrix square root finding. Neural Process Lett 50:1687–1703

    Article  Google Scholar 

  33. Khan A, Li S, Luo X (2019) Obstacle avoidance and tracking control of redundant robotic manipulator: an rnn based metaheuristic approach. IEEE Trans Ind Inf. https://doi.org/10.1109/TII.2019.2941916

    Article  Google Scholar 

  34. Khan AH, Cao X, Li S, Luo C (2019) Using social behavior of beetles to establish a computational model for operational management. IEEE Trans Comput Soc Syst. https://doi.org/10.1109/tcss.2019.2958522

    Article  Google Scholar 

  35. Zhang Y, Li S, Zou J, Khan AH (2019) A passivity-based approach for kinematic control of redundant manipulators with constraints. IEEE Trans Ind Inf. https://doi.org/10.1109/tii.2019.2908442

    Article  Google Scholar 

  36. Xu Z, Li S, Zhou X, Zhou S, Cheng T (2020) Dynamic neural networks for motion-force control of redundant manipulators: an optimization perspective. IEEE Trans Ind Electron. https://doi.org/10.1109/TIE.2020.2970635

    Article  Google Scholar 

  37. Xu Z, Li S, Zhou X, Yan W, Cheng T, Huang D (2019) Dynamic neural networks based kinematic control for redundant manipulators with model uncertainties. Neurocomputing 329(15):255–266

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Guangdong Key Laboratory of Modern Control Technology, Guangdong Institute of Intelligent Manufacturing, Guangzhou, 510070, Guangdong, People’s Republic of China

    Xiaoxiao Li, Zhihao Xu & Xuefeng Zhou

  2. School of Engineering, Swansea University, Swansea, UK

    Shuai Li

  3. Foshan Tri-Co Intelligent Robot Technology Company Ltd., Foshan, 528300, People’s Republic of China

    Shuai Li, Zhihao Xu & Xuefeng Zhou

Authors
  1. Xiaoxiao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuai Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhihao Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xuefeng Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xuefeng Zhou.

Additional information

Publisher's Note

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

This work is supported by Guangdong Province Key Areas R&D Program (Grant No. 2019B090919002), Foshan Key Technology Research Project (Grant No. 1920001001148), Foshan Innovation and Entrepreneurship Team Project (Grant No. 2018IT100173), Guangzhou Science Research Plan-Major Project (Grant No. 201804020095), Guangdong Innovative Talent Project of Young College (Grant No. 2016TQ03X463), GDAS’ Project of Thousand doctors (postdoctors) Introduction (2019GDASYL-0103078), GDAS’ Project of Science and Technology Development (2018GDASCX-0115)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Li, S., Xu, Z. et al. A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application. Neural Process Lett 53, 1287–1304 (2021). https://doi.org/10.1007/s11063-021-10440-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-021-10440-x

Keywords

Search

Navigation