Improved stacked sparse auto encoder by adaptive shuffled shepherd optimization algorithm for ERT image reconstruction

Abstract Electrical resistance tomography (ERT) is a visualization detection technology possessing unique advantages in industrial dynamic process monitoring. ERT image reconstruction is an ill-posed inverse problem that is nonlinear and susceptible to measurement noise. In recent years, deep learning models have been used to learn complex patterns and relationships in sparse data, leading to more accurate and robust image reconstruction. A stacked sparse autoencoder (SSAE) based deep learning approach is proposed to map complex nonlinear functions for ERT image reconstruction. In the proposed SSAE, global conditional gradient steps employ the back projection concept and non-convex local search with the predicted model for fine-tuning. The proposed method guarantees a global optimum for the neural network sparse constraint. The model improves feature learning and representation, while the optimization technique effectively optimizes weight changes during training. A sparse autoencoder imposes sparsity constraints on the hidden layers to minimize the sparsity effect. The proposed model uses adaptive strategies to dynamically shuffle and optimize encoder-decoder weights in the architecture and optimize sparse features by shuffle shepherd optimization (SSO), which can take advantage of the proposed model’s ability to capture complex hierarchical features and the SSO’s adaptability in optimizing autoencoder parameters. The fusion of SSAE and SSO algorithms obtains an average structural similarity (SSIM) index of 0.8371 under an optimal sparsity constraint of 0.047 for reconstruction. The proposed model significantly enhances reconstruction performance with a loss of convergence of 0.0124, providing faster training and robust image reconstruction capabilities for ERT. Simulation and experimental tests are conducted to evaluate the network’s generalization ability and practicality.