基于灰色BP神经网络的水泥强度预测模型研究

水泥强度的预测具有多变量、非线性和大时滞特性,因此传统线性回归方法的结果不准确。除此之外,传统的神经网络预测可能对少量样本不够精确。本文建立灰色BP模型,以此来预测水泥的强度。建立一个多因素灰色模型GM(1,N)用于水泥化学成分的样本数据进行预处理,得到新的数据来作为建立预测模型的样本数据,通过BP神经网络建立预测模型。最终通过建立的灰色BP神经网络预测模型来预测28天水泥强度。仿真结果表明:灰色BP预测模型的效果比BP预测的要准确。

Prediction Model of Cement Strength Based on Grey BP Network

The cement strength prediction has characteristics of multi-variable, nonlinearity and large time delay and the traditional linear regression method results in a poor prediction accuracy;in addition, the con-ventional BP neural network may not be accurate enough for a few samples.In this paper, the grey BP model was established to predict cement strength.Having multi-factor grey model GM (1,N) used to preprocess the sample data of cement' s chemical component so as to get new data for the prediction model established through the BP neural network.Simulation results of applying BP network model to predict 28-day cement strength show that, the effect of grey BP forecasting model is more accurate than that of BP.