GPT答：

7i24.cn

"A random forest algorithm based on cyclic sparsity and Slime Mould Algorithm (SMA) optimization was proposed and applied to gear fault diagnosis. Firstly, a cyclic sparse decomposition model was established for envelope analysis, and the envelope harmonic-to-noise ratio was calculated. Then, the feature vector was constructed by combining the envelope harmonic-to-noise ratio with time-domain, frequency-domain indicators, etc. The SMA algorithm was introduced to optimize two parameters of random forests for gear fault recognition. The improved RF method was compared with three other classification methods: Random Forests (RF), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN). The experimental results showed that the improved RF method can improve classification accuracy."