Predicting Screening Efficiency of Probability Screens Using KPCA-GRNN with WP-EE Feature Reconstruction

The screening system is a nonlinear and non-Gaussian complex system. To better characterize its attributes and improve the prediction accuracy of screening efficiency, this study involves the acquisition of the vibration signals and screening efficiency data under various operational conditions. Subsequently, empirical mode decomposition energy entropy (EMD-EE), variational mode decomposition energy entropy (VMD-EE), and wavelet packet energy entropy (WP-EE) features are extracted from the time series vibration signals, and three single input energy entropy-generalized regressive neural network (GRNN) prediction accuracy models are established and compared. Furthermore, we introduce the kernel principal component analysis (KPCA)-WP-EE feature reconstruction-GRNN prediction algorithm. This approach involves reconstructing the feature vector by optimizing WP-EE-GRNN prediction under varying parameters. The parameterized GRNN model is then predicted and analyzed through secondary reconstruction involving KPCA dimensionality reduction features. The results show that WP-EE-GRNN achieves superior prediction accuracy compared to box dimension (d)-GRNN, box dimension-back propagation neural network (BPNN), and d-weighted least squares support vector machine, WP-d-GRNN, WP-EE-BPNN, EMD-EE-GRNN, and VMD-EE-GRNN. Additionally, the WP-EE feature reconstruction-GRNN algorithm exhibits higher prediction accuracy than the single-input WP-EE-GRNN algorithm. The WP-EE-GRNN prediction algorithm using KPCA dimensionality reduction and secondary reconstruction not only achieves higher prediction accuracy than prior to KPCA dimensionality reduction but also improves prediction efficiency. Following the extraction of two core principal components, model parameters when KPCA’s σ2 = 0.85, the optimal parameter of GRNN model Spread = 0.051, and the optimal number of training samples N = 19, the average prediction error is 1.434%, the minimum prediction error reaching 0.708%, the minimum root mean square error reaching 0.836% and Pearson correlation coefficient marking the closest to 1, these result all representing the optimum achievable values. The budget model selects the optimal parameter combination scheme for the system.