AN Identification and Prediction Model Based on PSO
Hui Wang, Tie Cai, Dongsheng Cheng, Kangshun Li, Ying Chang Zhou · International Journal of Cognitive Informatics and Natural Intelligence · 2024
According to the spectral characteristics of different Chinese medicinal materials, the types of Chinese medicinal materials and the origin of Chinese medicinal materials are identified. Construct a fragmented clustering model. Firstly, the mid-infrared sample data is preprocessed, the Laida criterion model is established, and the abnormal data is eliminated; then the slicing model is used to divide the spectral wave into different regions according to the spectral characteristics. The data of each slice is clustered through the k-means clustering model. The origin of Chinese medicinal materials is identified by the support vector machine model. The data of Chinese medicinal materials with a known origin of a certain type of Chinese medicinal materials is used as the training sample set, and the data of Chinese medicinal materials with unknown origin is used as the test set.