基于模糊神经网络的复方模式识别研究

Yi-Bo Li, Sen-Yue Zhang
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引用次数: 0

摘要

提出了一种基于神经网络和方剂专家系统的中成药模式识别新方法。该方法首先在均匀条件下对处方制剂进行色谱实验,然后对各基团的分离峰进行分离分析。利用模糊神经网络识别出一些可能存在的中药。方剂专家系统根据已识别的药材集和模糊神经网络输出的可能药材可信度,提出最可能需要进一步识别的药材。在特异性的基础上对最可能的药材进行色谱实验,根据特异性指标验证可能的药材是否为期望的药材,同时计算出本处方中药材的比例。通过循环上述方法,可以识别所有的组合药材及其在复方药材中的比例,从而识别处方。通过实际的复方试验,验证了该方法的有效性和准确性。此外,该方法还为复方分析提供了一种新的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on Compound Prescriptions Pattern Recognition Based on Fuzzy Neural Network
CA new method of Chinese patent medicine pattern recognition based on Neural Network and prescriptions Expert System is presented. In this method, first, chromatogram experiments are done on a prescriptions preparation at uniform condition and then, peak separation of each group is separated and analyzed. Some possible traditional Chinese medicines are recognized over the use of fuzzy neural network. Prescriptions Expert System proposes the most possible medicinal herbs needed to be further recognized on the basis of the recognized medicinal herbs set and credibility of the possible medicinal herbs outputted by fuzzy neural network. Chromatogram experiments on the basis of specificity of are done on the most possible medicinal herbs in order to verify whether the possible medicinal herbs are the expected ones according to specificity index, the proportion of the medicinal herbs in this prescription can be calculated simultaneously. By circulating above-mentioned method, all the assembled medicinal herbs and their proportions in compounded medicinal herbs can be recognized and consequently prescriptions are recognized. The efficiency and accuracy of this method is verified by actual compound preparation testing. Furthermore, the method is shown to be able to provide a new approach to analysis of compound prescriptions.
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