title: [Identification of rhubarb samples based on IR spectra by using Takagi-Sugeno fuzzy systems].
abstract: 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.
authors.affiliation: Department of Chemistry Resources Environment and GIS Key Lab of Beijing, Capital Normal University, Beijing, China.
authors.forename: Yan-feng
authors.initials: YF
authors.lastname: Tang
authors.forename: Zhuo-yong
authors.initials: ZY
authors.lastname: Zhang
authors.forename: Guo-qiang
authors.initials: GQ
authors.lastname: Fan
mesh_terms: D004365:Drugs, Chinese Herbal / Q000032:analysis* / Q000592:standards; D017143:Fuzzy Logic; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012250:Rheum / Q000737:chemistry*; D013055:Spectrophotometry, Infrared / Q000379:methods*
chemical_list: D004365:Drugs, Chinese Herbal