Recognition of Key Information in Non-Stationary Signals Based on Wavelet Threshold Denoising and Back Propagation Neural Network Optimized by Manta Ray Foraging Optimization Algorithm

The identification of key information hidden in non-stationary signals is challenging in various fields such as logistics and transportation, biomedicine, and fault diagnosis. To facilitate this identification, we propose a back propagation neural network (BPNN) recognition algorithm based on wavelet threshold denoising (WTD) and manta ray foraging optimization (MRFO) algorithm for the first time. The algorithm first performs WTD on the original signals, which can better extract features of the original signals. Subsequently, in order to improve the convergence speed of recognition model, MRFO algorithm is used to optimize the initial weights and thresholds of BPNN. On the base of this, the optimization model is finally obtained to recognize the key information in non-stationary signals. The comparative experimental results indicate that the proposed WTD-MRFO-BPNN algorithm has higher performance in key information recognition. The recognition accuracy reaches 97.25%.