Traditional Chinese Medicine Pattern Recognition and Quality Evaluation Based on a Supervised Fuzzy ART
基于监督模糊ART的中药材模式识别和质量评价

A supervised fuzzy ART is presented for recognizing traditional Chinese medicines and evaluating its quality based on high performance liquid chromatogram(HPLC). It has two improvements to a fuzzy ART--is turn first to a supervised fuzzy ART then is set a single vigilance parameter for every clustering-center (sub-class), therefore a fuzzy ART becomes a supervised fuzzy ART with the automatic clustering ability to sub-class. The supervised fuzzy ART have the performances and advantages as the same as a fuzzy ART, for example, the network doesn't learn again while a new class must be added to it. The automatic clustering ability to sub-class has great meaning to the pattern recognition of traditional Chinese medicine because the traditional Chinese medicine will be very different under the condition of different place come from, different time picked up and different method dealt with. It is manifested after abundant of traditional Chinese medicine samples have been used to test that the network's abilities, such as the resistance to parallel removal, deformation and the adaptability to the medicines coming from new places, are very strong. So, it indicts the arrival to the prospective.