Performance Optimization and Interpretability of Recurrent Sigma-Pi-Sigma Neural Networks on Application of IoE Data

With the exponential growth of Internet of Things (IoT) devices in the era of Internet of Everything (IoE), two major issues arise: 1) data processing speed and 2) interpretability in neural networks. Specifically, training neural networks to handle IoE data often results in poor convergence speed, overfitting of the weights, and fluctuations in the error function. To address these challenges, this article introduces a novel neural network, the recurrent sigma-pi-sigma neural network (RSPSNN), trained using a batch gradient algorithm enhanced with smoothing L1 lasso regularization and an adaptive momentum term. This approach not only improves convergence speed but also enhances generalization capabilities and reduces oscillations. Furthermore, the interpretability of RSPSNN is theoretically demonstrated through characteristics of monotonicity, strong/weak convergence, and stability. Finally, the theoretical findings are supported by experiment results in classification, recognition, and prediction tasks.