title: [Study on the antioxidation property of engine oil by infrared spectroscopy].
abstract: The relationship between the engine oil' s infrared spectroscopic characteristics and its antioxidation property was studied, and the technical method to obtain the spectral information in accordance with the contribution of lubricating-oil composition to antioxidation property is presented. Quantitative self-organization neural networks was developed by combining the advantages of BP neural networks and those of self-organization neural networks. Quantitative self-organization neural networks possesses both the qualitative clustering function of self-organization neural networks and the quantitative analysis function of BP neural networks, and featuFes better robustness, so the quantitative analysis result of quantitative self-organization neural networks was better than that of BP neural networks. The result has provided a kind of new technology means for fast testing lubricating-oil performance.
authors.affiliation: Fuel and Oil Testing Center, Logistic Engineering University, Chongqing 400016, China. hgycfx@163.com
authors.forename: Zi-cun
authors.initials: ZC
authors.lastname: Li
authors.forename: Xin-lu
authors.initials: XL
authors.lastname: Feng
authors.forename: Gang
authors.initials: G
authors.lastname: Xiong
mesh_terms: D000975:Antioxidants / Q000032:analysis*; D005648:Fuel Oils / Q000032:analysis*; D016571:Neural Networks, Computer; D013055:Spectrophotometry, Infrared / Q000379:methods*
chemical_list: D000975:Antioxidants; D005648:Fuel Oils