Gas emission prediction model based on genetic algorithm and fuzzy multivariate linear regression analysis
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摘要: 针对经典线性回归模型不能完全反映变量间的耦合关系而不适宜于有模糊数的瓦斯涌出量预测的问题,提出了一种基于遗传算法模糊多元线性回归分析的瓦斯涌出量预测模型。采用灰关联分析法和SPSS软件线性回归分析法确定影响瓦斯涌出量的主要因素;把历史数据样本分为建模数据样本和检测数据样本,采用遗传算法求出模糊回归参数的中心值和模糊幅值。实验结果表明,该模型具有较高的精确度和可操作性。Abstract: In view of problem that classical linear regression model cannot completely reflect coupling relationship among variables and it is not suitable for gas emission prediction with fuzzy number, the paper proposed a gas emission prediction model based on genetic algorithm and fuzzy multivariate linear regression analysis. The model uses gray relation analysis method and SPSS software linear regression analysis method to determine main factors influencing on gas emission, and divides history data sample into modeling data sample and detection data sample and uses genetic algorithm to get center value and fuzzy amplitude value of fuzzy regression parameters. Experiment result shows that the model has higher precision and operability.
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