A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 10
Issue 8
IEEE/CAA Journal of Automatica Sinica
| Citation: | Z. H. Hao, G. C. Wang, B. Zhang, L. Y. Fang, and H. S. Li, “An isomerism learning model to solve time-varying problems through intelligent collaboration,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 8, pp. 1772–1774, Aug. 2023. doi: 10.1109/JAS.2023.123360
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| [1] |
G. Wang, Z. Hao, B. Zhang, and L. Jin, “Convergence and robustness of bounded recurrent neural networks for solving dynamic Lyapunov equations,” Information Sciences, vol. 588, pp. 106–123, Apr. 2022. doi: 10.1016/j.ins.2021.12.039
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| [2] |
M. Liu, X. Zhang, M. Shang, and L. Jin, “Gradient-based differential kWTA network with application to competitive coordination of multiple robots,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1452–1463, Aug. 2022. doi: 10.1109/JAS.2022.105731
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| [3] |
L. Jin, L. Wei, and S. Li, “Gradient-based differential neural-solution to time-dependent nonlinear optimization,” IEEE Trans. Autom. Control, vol. 68, no. 1, pp. 620–627, 2023. doi: 10.1109/TAC.2022.3144135
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| [4] |
S. Liao, J. Liu, X. Xiao, D. Fu, G. Wang, and L. Jin, “Modified gradient neural networks for solving the time-varying Sylvester equation with adaptive coefficients and elimination of matrix inversion,” Neurocomputing, vol. 379, pp. 1–11, Feb. 2020. doi: 10.1016/j.neucom.2019.10.080
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| [5] |
G. Wang, Q. Li, S. Liu, H. Xiao, and B. Zhang, “New zeroing neural network with finite-time convergence for dynamic complex-value linear equation and its applications,” Chaos,Solitons &Fractals, vol. 164, p. 112674, Nov. 2022.
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| [6] |
G. Wang, Z. Hao, H. Huang, and B. Zhang, “A proportional-integral iterative algorithm for time-variant equality-constrained quadratic programming problem with applications,” Artificial Intelligence Review, vol. 56, pp. 4535–4556, 2023. doi: 10.1007/s10462-022-10284-4
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| [7] |
L. Jin, X. Zheng and X. Luo, “Neural dynamics for distributed collaborative control of manipulators with time delays,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 854–863, May 2022. doi: 10.1109/JAS.2022.105446
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L. Jin, S. Li, B. Liao, and Z. Zhang, “Zeroing neural networks: A survey,” Neurocomputing, vol. 267, pp. 597–604, Dec. 2017. doi: 10.1016/j.neucom.2017.06.030
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| [9] |
M. Liu, S. Li, and L. Jin, “Modeling and analysis of Matthew effect under switching social networks via distributed competition,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1311–1314, Jul. 2022. doi: 10.1109/JAS.2022.105527
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| [10] |
S. Warnat-Herresthal, H. Schultze, K. L. Shastry, S. Manamohan, S. Mukherjee, et al., “Swarm learning for decentralized and confidential clinical machine learning,” Nature, vol. 594, pp. 265–270, May 2021. doi: 10.1038/s41586-021-03583-3
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| [11] |
Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Trans. Intelligent Systems and Technology (TIST), vol. 10, no. 2, pp. 1–19, Mar. 2019.
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| [12] |
J. Dai, L. Jia, and L. Xiao, “Design and analysis of two prescribed-time and robust ZNN models with application to time-variant Stein matrix equation,” IEEE Trans. Neural Networks Learning Systems, vol. 32, no. 4, pp. 1668–1677, Apr. 2021. doi: 10.1109/TNNLS.2020.2986275
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B. N. Datta, Numerical Methods for Linear Control Systems. Amsterdam, Netherlands: Elsevier Academic Press, 2004.
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