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Inverse-free zeroing neural network for time-variant nonlinear optimization with manipulator applications.

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October 2024

School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China. Electronic address:

In this paper, the problem of time-variant optimization subject to nonlinear equation constraint is studied. To solve the challenging problem, methods based on the neural networks, such as zeroing neural network and gradient neural network, are commonly adopted due to their performance on handling nonlinear problems. However, the traditional zeroing neural network algorithm requires computing the matrix inverse during the solving process, which is a complicated and time-consuming operation.

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Compared with conventional dynamic nonlinear equation systems, a hybrid double-deck dynamic nonlinear equation system (H3DNES) not only has multiple layers describing more different tasks in practice, but also has a hybrid nonlinear structure of solution and its derivative describing their nonlinear constraints. Its characteristics lead to the ability to describe more complicated problems involving multiple constraints, and strong nonlinear and dynamic features, such as robot manipulator tracking control. Besides, noises are inevitable in practice and thus strong robustness of models solving H3DNES is also necessary.

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Background: Usual measures of blood pressure (BP) do not account for both the magnitude and duration of exposure to elevated BP over time. We aimed to demonstrate the effect of a novel time-weighted BP on cardiovascular outcomes using a post hoc analysis of two published randomized trials.

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It is perhaps most useful to approach the Doctor-Patient relationship (DPR) by admitting that it's complicated. We review some of the strategies that have been employed to mitigate this complexity, zeroing in on one that promises to capture the main features of the DPR without eliminating some of its more important, existential components; pieces of the puzzle that must be retained if we are to avoid oversimplification and the errors that can arise by ignoring important foundational properties. We believe that a useful way to look at the DPR and to capture essential features that must be balanced in the process is provided by Partnership Theory and its definition in terms of the so-called domination and partnership systems.

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Robustness is a significant research direction in manipulator control owing to their complicated and uncertain external environment, abrasion, and other factors. The ability to implement multitasking is also necessary for manipulator control because of the physical limitations and complex requirements. However, the existing research has mainly focused on the control of a single task and robustness analysis of single-task control.

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