Electrical Appliance Recognition Algorithm Based on Neural Network and Kernel Density Estimation

Abstract

With the rapid development of the social economy, the need for social intelligent transformation is increasingly urgent. In order to identify the electrical equipment quickly and accurately and reduce the false recognition rate, we propose a new identification algorithm, which uses the neural network model to pre-identify the connected electrical equipment and then conducts a secondary test through the kernel density estimation model. The experimental results show that the algorithm has a high recognition rate for the trained model and, at the same time, solves the misrecognition problem of the new electrical appliance neural network model in the production environment.