Modified Newton Integration Neural Algorithm for Solving Time-Varying Yang-Baxter-Like Matrix Equation

Huang, Haoen; Huang, Zifan; Wu, Chaomin; Jiang, Chengze; Fu, Dongyang; Lin, Cong

doi:10.1007/s11063-022-10908-4

Cited by 1 publication

(2 citation statements)

References 33 publications

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“…First, the noises in experiments are set to [5] 2×2 and injected into the model as described in (11). As shown in Fig.…”

Section: In the Case Of Constant Noisesmentioning

confidence: 99%

“…Many practical engineering applications or problems can be transformed into a potential DMSR problem, which attracted an increasing number of researchers for these topics, replacing time-independent problems. Among many works, the zeroing neural network (ZNN) is one of the most comprehensively investigated ways to resolve dynamic problems [5][6][7][8][9]. Further, the existing researches show that the ZNN model can possess superior performance on both sides of parallel computing and convergence speed [10].…”

Section: Introductionmentioning

confidence: 99%

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Robust neural dynamics with adaptive coefficient applied to solve the dynamic matrix square root

Jiang

Xiao

et al. 2022

Complex Intell. Syst.

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Zeroing neural networks (ZNN) have shown their state-of-the-art performance on dynamic problems. However, ZNNs are vulnerable to perturbations, which causes reliability concerns in these models owing to the potentially severe consequences. Although it has been reported that some models possess enhanced robustness but cost worse convergence speed. In order to address these problems, a robust neural dynamic with an adaptive coefficient (RNDAC) model is proposed, aided by the novel adaptive activation function and robust evolution formula to boost convergence speed and preserve robustness accuracy. In order to validate and analyze the performance of the RNDAC model, it is applied to solve the dynamic matrix square root (DMSR) problem. Related experiment results show that the RNDAC model reliably solves the DMSR question perturbed by various noises. Using the RNDAC model, we are able to reduce the residual error from 10$$^1$$ 1 to 10$$^{-4}$$ - 4 with noise perturbed and reached a satisfying and competitive convergence speed, which converges within 3 s.

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“…First, the noises in experiments are set to [5] 2×2 and injected into the model as described in (11). As shown in Fig.…”

Section: In the Case Of Constant Noisesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust neural dynamics with adaptive coefficient applied to solve the dynamic matrix square root

Jiang

Xiao

et al. 2022

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

Modified Newton Integration Neural Algorithm for Solving Time-Varying Yang-Baxter-Like Matrix Equation

Cited by 1 publication

References 33 publications

Robust neural dynamics with adaptive coefficient applied to solve the dynamic matrix square root

Robust neural dynamics with adaptive coefficient applied to solve the dynamic matrix square root

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