These efforts are promising but limited. AI minds don't necessarily map to human concepts. Some believe that we need to ...

Recent studies have demonstrated the effectiveness of supervised learning in spiking neural networks (SNNs). A trainable SNN provides a valuable tool not only for engineering applications but also for ...

A team of scientists in the United States has combined both spatial and temporal attention mechanisms to develop a new approach for PV inverter fault detection. Training the new method on a dataset ...

This example shows how to build and train a convolutional neural network (CNN) from scratch to perform a classification task with an EEG dataset.

We present a method to train neural network controllers with guaranteed stability margins. The method is applicable to linear time-invariant plants interconnected with uncertainties and nonlinearities ...

Here we proposed a hybrid neural network (Hybrid-NN) as a novel scheme to improve the detection performance in terms of validation accuracy and required training data amount. The idea is to insert ...