Abstract
IFHI (Fire Risk Index) is a key indicator of the fire risk capability of polymer materials.[1] In view of the complexity, uncertainty and nonlinearity of IFHI prediction, this paper adopts the grey relational analysis method proposed by Deng Yulong et al to carry out dimensionality reduction analysis of parameters. Four parameters of SEA, MLR, TTI and CO yield, which are highly correlated with IFHI value, were selected to construct BP neural network prediction model. The predicted value is compared with the actual value, and the mean square error MSE =0.01308. It is proved that this model has a good prediction effect and can provide scientific basis for IFHI prediction.
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