AN Identification and Prediction Model Based on PSO

Abstract

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.