红外光谱与高-木关野系统结合鉴别大黄的研究

Identification of Rhubarb Samples Based on IR Spectra by Using Takagi-Sugeno Fuzzy Systems

高木-关野模糊系统是基于若干反向传播神经网络(BP ANN)组成的,它具有一些模糊逻辑特性。文章利用红外光谱与高木关野模糊系统相结合鉴别5 2种大黄样品。并对神经网络的隐含层个数和动量因子的影响作了讨论。结果表明,用高木 关野模糊系统得到的结果比通常用的BP网络要好。选用适当的网络训练参数,正确率可达到10 0 %。该方法比常规方法更准确,比民间传统方法更具科学性,因此是鉴别大黄的一种快速、简便的方法。

Takagi-Sugeno fuzzy system is composed of several back-propagation neural networks (BP-NNs), and has some fuzzy logic properties. In this paper, the Takagi-Sugeno fuzzy logic system is applied to identifying official and unofficial rhubarb samples based on their infrared spectra. The effects of the number of hidden neurons and the momentum parameters on the prediction were investigated. The results obtained by using Takagi-Sugeno fuzzy system were better than those by commonly used BP-networks. With a proper network training parameter, 100% correctness can be obtained by using Takagi-Sugeno fuzzy system. This method is more accurate than the common methods, and is more scientific than traditional methods. So it is applied to identifying rhubarb easily and rapidly.