Yongbai Liu

Orcid: 0000-0002-4118-047X

According to our database1, Yongbai Liu authored at least 10 papers between 2020 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
Generalized zeroing neural dynamics model for online solving time-varying cube roots problem with various external disturbances in different domains.
Inf. Sci., April, 2023

sEMG-based continuous motion estimation of upper limb using a novel noise-tolerant zeroing neurodynamic model combined with LSTM network.
Digit. Signal Process., March, 2023

Noise-tolerant zeroing neurodynamic algorithm for upper limb motion intention-based human-robot interaction control in non-ideal conditions.
Expert Syst. Appl., 2023

2022
Discrete-time noise-tolerant Z-type model for online solving nonlinear time-varying equations in the presence of noises.
J. Comput. Appl. Math., 2022

Different discrete-time noise-suppression Z-type models for online solving time-varying and time-invariant cube roots in real and complex domains: Application to fractals.
Neurocomputing, 2022

sEMG-based multi-joints motion estimation of lower limb utilizing deep convolutional neural network.
Proceedings of the IEEE International Conference on Real-time Computing and Robotics, 2022

Upper Limb Multi-Joint Angle Estimation Based on Multichannel sEMG Signals Using Elman Neural Network.
Proceedings of the IEEE International Conference on Real-time Computing and Robotics, 2022

Disturbance Observer Compensation Based on Sliding-Mode Approach for Solving Compliant Actuator Tracking Control Problems.
Proceedings of the Intelligent Robotics and Applications - 15th International Conference, 2022

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

2020
Two DTZNN Models of O(τ<sup>4</sup>) Pattern for Online Solving Dynamic System of Linear Equations: Application to Manipulator Motion Generation.
IEEE Access, 2020


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