Titlebook: Neural Dynamics for Time-varying Problems; Advances and Applica Long Jin,Lin Wei,Xin Lv Book 2025 The Editor(s) (if applicable) and The Aut

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Neural Dynamics Based on Control Theoretical Techniques,ional models. From the control theoretical perspective, a computational method for solving an algebraic can be deemed as a controller, of which the error should converge to zero, and subsequently, the controller’s output is identical to the theoretical solution of the algebraic equation. This chapte

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Noise-Tolerant Neural Dynamics,mputational models are presented for solving them under the hypothesis of short-time invariance. To eliminate the large lagging error in the solution of the inherently dynamic nonlinear optimization problem, the only way is to estimate the future unknown information by using the present and previous

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High-Order Robust Discrete-Time Neural Dynamics,s-time one and depend on the Euler difference formula, which cannot apply to essentially discrete problems and have low solution accuracy. Moreover, these methods all focus on static problems, not consider time-varying ones, and thus may have unsatisfactory performance in applications with time-vary

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Book 2025al analysis, simulative examples, and physical experiments. Based on these methods, their applications include motion planning of redundant manipulators, filter design, winner-take-all operation, multiple-input multiple-output system configuration, multi-linear tensor equation solving, and manipulab

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