This study proposes a novel method for designing prosthetic heart valves (PHVs) by combining machine learning (ML) with optimization algorithms. This approach aims to overcome the limitations of ...
Many controlled processes, such as biochemical ones, are repetitive, similar to batch-organized processes. They generate Optimal Control Problems (OCPs) solved by optimal controllers, which often ...
Abstract: This paper presents a study of different fuzzy neural network (FNN) learning control methods for brushless dc (BLDC) motor drives. The FNN combines fuzzy logic (FL) with the learning ...
Deep neural networks (DNNs) are a class of artificial neural networks (ANNs) that are deep in the sense that they have many layers of hidden units between the input and output layers. Deep neural ...
Abstract: As the core equipment for AC/DC conversion in ultra-high voltage direct current (UHVDC) transmission systems, thyristor converter valves are the main source of losses in converter stations.
The fault diagnosis of the inverter is fundamental to energy intelligence. Due to the complex characteristics of the inverter (e.g., high-dimensional decision and poor stability), it is challenging to ...
In view of the dynamics of the dam safety monitoring data, the sensitivity to time and space, and the nonlinearity, it has been proposed to use the firefly algorithm to search to determine the delay ...
Previously, we found that in the mammalian retina, inhibitory inputs onto starburst amacrine cells (SACs) are required for robust direction selectivity of On-Off direction-selective ganglion cells (On ...
Sustained changes in mood or action require persistent changes in neural activity, but it has been difficult to identify the neural circuit mechanisms that underlie persistent activity and contribute ...
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