Automatic DNN architecture design using CPSOTJUTT for power system inspection

Abstract

AbstractTo quickly and accurately automatically design more high-precision deep neural network models (DNNs), this paper proposes an automatic DNN architecture design ensemble model based on consensus particle swarm optimization-assisted trajectory unified and TRUST-TECH (CPSOTJUTT), called CPSOTJUTT-EM. The proposed model is a three-layer model, and its core is a three-stage method for addressing the sensitivity of the local solver to the initial point and enabling fast and robust training DNN, effectively avoiding missing high-quality DNN models in the process of automatic DNN architecture design. CPSOTJUTT has the following advantages: (1) high-quality local optimal solutions (LOSs) and (2) robust convergence against random initialization. CPSOTJUTT-EM consists of the bottom layer: stable and fast design high-quality DNN architectures, the middle layer: exploration for a diverse set of optimal DNN classification engines, and the top layer: ensemble model for higher performance. This paper tests the performance of CPSOTJUTT-EM on public datasets and three self-made power system inspection datasets. Experimental results show that the CPSOTJUTT-EM has excellent performance in automatic DNN architecture design, DNN model optimization. And the CPSOTJUTT-EM can automatically design high-quality DNN ensemble models, laying a solid foundation for the application of DNN in other fields.