ZNN for solving online time-varying linear matrix–vector inequality via equality conversion
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DOI: 10.1016/j.amc.2015.02.060
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Cited by:
- Zhu, Jingcan & Jin, Jie & Chen, Weijie & Gong, Jianqiang, 2022. "A combined power activation function based convergent factor-variable ZNN model for solving dynamic matrix inversion," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 197(C), pages 291-307.
- Qi, Zhaohui & Ning, Yingqiang & Xiao, Lin & Luo, Jiajie & Li, Xiaopeng, 2023. "Finite-time zeroing neural networks with novel activation function and variable parameter for solving time-varying Lyapunov tensor equation," Applied Mathematics and Computation, Elsevier, vol. 452(C).
- Bolin Liao & Cheng Hua & Xinwei Cao & Vasilios N. Katsikis & Shuai Li, 2022. "Complex Noise-Resistant Zeroing Neural Network for Computing Complex Time-Dependent Lyapunov Equation," Mathematics, MDPI, vol. 10(15), pages 1-17, August.
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Keywords
Zhang neural network (ZNN); Time-varying; Linear matrix–vector inequality (LMVI); Conversion; ZNN design formula;All these keywords.
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