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Prediction for Working Performance of Air-and-screen Cleaning Unit Based on the epsilon-SVR Method

机译:基于epsilon-SVR方法的风幕清洗机工作性能预测

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On the basis of analyzing disadvantages of conventional prediction model of air-and-screen cleaning device, a new regression model based on support vector machine was proposed to predict and control of cleaning process precisely. Parameters of e-SVR models were determined utilizing non-heuristic Grid Search, heuristic GA and PSO which could avoid the choice of randomness. The effect of samples in different on prediction performance of e- SVR was analyzed compared with BP. The results indicate that the prediction property of e-SVR is better than BP especially in small-sample.
机译:在分析传统的风幕清洗设备预测模型的弊端的基础上,提出了一种基于支持向量机的回归模型,用于对清洗过程的精确预测和控制。使用非启发式网格搜索,启发式GA和PSO可以确定e-SVR模型的参数,从而避免选择随机性。与BP相比,分析了不同样本对e-SVR预测性能的影响。结果表明,e-SVR的预测性能优于BP,特别是在小样本情况下。

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