Classification method of f tea leaves based on possibility fuzzy identification C-means clustering

Abstract

The invention discloses a classification method of infrared spectrums of tea leaves based on possibility fuzzy identification C-means clustering. Infrared spectrum data of tea samples are collected byusing a Fourier infrared spectrum analyzer; the infrared spectrum data of the tea samples are preprocessed; dimension reduction processing is carried out on the infrared spectrum data of the tea samples after preprocessing by using a principal component analysis method; and identification information of the infrared spectrums of the tea training samples is extracted by using linear identificationanalysis. Possibility fuzzy identification C-means clustering is carried out on the training sample in the step four to obtain a clustering center; and tea class determination is carried out by usingthe possibility fuzzy identification C-means clustering method. According to the invention, on basis of combination of possibility fuzzy C-means clustering and linear discriminant analysis, the method has advantages of fast detection speed, fast classification speed and high classification accuracy and the like and is used for realizing correct classification of tea varieties.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, and in particular relates to a tea classification method that can fuzzily identify C-means clustering. Background technique [0002] Tea has long been a drink for daily health care. It has the effects of promoting body fluid and quenching thirst, refreshing and improving thinking, anti-inflammatory and detoxifying, sobering up and strengthening the heart. With the improvement of living standards, people have higher and higher requirements for the quality of tea leaves. However, in the face of such a huge number of tea varieties, it is difficult to distinguish their pros and cons. In addition, counterfeit and shoddy tea is common in the Chinese market, which has brought certain damages to the interests of both tea producers and consumers. Therefore, it has been an important task for scientific researchers to develop a simple, fast and highly accurate method for discriminating the ...

AI Technical Summary

Problems solved by technology

FCM based on the minimum square error criterion can cluster linearly separable data. However, FCM is sensitive to noise. In order to overcome this shortcoming, Krishnapuram and Keller abandoned the possibility constraints of FCM and constructed a new target function, the possibility of C-means clustering (PCM) is proposed. PCM can cluster data containing noise or outliers, and make the noise data a small membership value, so the impact of noise on clustering can be ignored. But PCM is very sensitive to the initial clustering center, which often leads to consistent clustering results. In order to overcome the shortcomings of FCM and PCM that are sensitive to noise data and produce consistent clustering, Pal et al. proposed the possibility fuzzy C on the basis of FCM and PCM. - mean clustering (PFCM)

However, PFCM cannot dynamically extract identification information and change the dimensionality of data during the clustering process.

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