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Comput Intell Neurosci ; 2022: 3224490, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35785091

RESUMEN

Multimodal orbital angular momentum is a research hotspot in the field of electromagnetic wave communication. How to accurately detect and identify multimodal orbital angular momentum data is a current academic problem. Based on the theory of mechanically reconfigurable arrays and neural networks, the purity, detection method, and transmission and reception of orbital angular momentum vortex waves are modeled in this paper. Through the network identification of the dynamic model of the three-degree-of-freedom reconfigurable manipulator, the paper takes the identification result and the control input of the single neuron PID as the input of the system control torque of the manipulator and realizes the reconfigurable manipulator. In the simulation process, the local approximation effect of the nonlinear control system used is very ideal. The single neuron PID controller overcomes the shortcomings of time-consuming and unsatisfactory control accuracy caused by the constant parameter of the traditional PID controller and realizes the circular loop. On the other hand, at the point of interest of the human eye, its resolution value is the largest, and its value gradually decreases as the distance from the pit increases. The experimental results show that the three-transmitting and three-receiving orbital angular momentum vortex wave transceiver system based on the mechanically reconfigurable array and neural network theory is relatively complete, and the transmission coefficient between the same modes reaches 0.827, which is much higher than that between different modes. On this basis, the modal purity, detection method, and reception of orbital angular momentum are studied accordingly. At the same time, the damage to the microscopic particles can be greatly reduced. At the same time, the information delay is reduced to 8.25%, which effectively improves the isolation characteristics of different modal orbital angular momentum channels and promotes the communication transmission of multimodal signals.


Asunto(s)
Redes Neurales de la Computación , Neuronas , Simulación por Computador , Humanos , Movimiento (Física) , Extremidad Superior
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