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论文类型：会议论文

发表时间：2014-05-08

收录刊物：EI、CPCI-S、Scopus

页面范围：581-584

关键字：solution polymerized styrene-butadiene rubber; abrasion resistance; artificial neural networks; multilayer feed-forward neural networks; prediction

摘要：Abrasion resistance of solution polymerized styrene-butadiene rubber (SSBR) based composites is a typical and crucial property in practical applications. Previous studies show that the abrasion resistance can be calculated by the multiple linear regression model. In our study, considering this relationship can also be described into the non-linear conditions, a Multilayer Feed-forward Neural Networks model with 3 nodes (MLFN-3) was successfully established to describe the relationship between the abrasion resistance and other properties, using 23 data groups, with the RMS error 0.07. Our studies have proved that Artificial Neural Networks (ANN) model can be used to predict the SSBR-based composites, which is an accurate and robust process.