GPT答：

7i24.cn

Propose an improved random forest algorithm based on cyclic sparsity and slime mould algorithm, and apply it to gear fault diagnosis. Firstly, a cyclic sparse decomposition model is established for envelope analysis, and the envelope harmonic-to-noise ratio is calculated. Then, the envelope harmonic-to-noise ratio and other indicators such as time-domain and frequency-domain are used to construct feature vectors. The slime mould algorithm (SMA) is introduced to optimize two parameters of random forests (RF) for gear fault recognition. The performance of the proposed method is compared with three other classification methods: RF, long short-term memory network (LSTM), and convolutional neural network (CNN). Experimental results show that the improved RF can significantly improve classification accuracy.