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 features 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.
李子存, 冯新泸, 熊刚. 红外光谱评价内燃机油抗氧化性能的研究[J]. 光谱学与光谱分析, 2007, 27(03): 444-447.
LI Zi-cun, FENG Xin-lu, XIONG Gang. Study on the Antioxidation Property of Engine Oil by Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2007, 27(03): 444-447.
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