Modeling an asynchronous machine based on artificial intelligence multi-models

In this paper, we propose a neural network-based modeling approach to achieve a model of an asynchronous machine. In addition, an adaptation is used for superior modeling estimation. The proposed approach is based on the use of multi-neural networks, so that the number of neural networks is set according to the learning error measurements of a single neural network. In addition, the effectiveness of a multi-neural network model has been verified by comparison with a single-neural network model. This new methodology offers a precise model that can be used in diagnostic, monitoring, and supervision systems.