Artificial neural networks, known as ANNs, which are algorithms designed to emulate the behavior of brain neurons could aid in combating cognitive impairments.
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Future hip fracture assessment may be led by machines as researchers build a machine that outperforms clinicians in identifying and classifying hip fractures, hinting at better care and reduced costs.
Abstract
In this paper, we investigate the problem of trajectory tracking control for marine surface vehicles (MSVs), which is subject to dynamic uncertainties, external disturbances and unmeasurable velocities. To recover the unmeasurable velocities, a novel adaptive neural network (NN) state observer is constructed. To guarantee the transient and steady-state tracking performance, a novel nonlinear transformation method is proposed by employing a tracking error transformation together with a newly constructed performance function, which is featured by user-defined settling time and tracking control accuracy. With the aid of the state observer and the nonlinear transformation method, with the combination of the adaptive NN technique and vector-backstepping design tool, an adaptive neural output feedback trajectory tracking control scheme with predefined performance is developed Referring to our developed control scheme, uncertainties can be reconstructed only by utilizing the posit