In order to know the ventilating capacity of imperial smelt furnace(ISF), and increase the output of plumbum, an intelligent modeling method based on gray theory and artificial neural networks(ANN) is proposed, in whi...In order to know the ventilating capacity of imperial smelt furnace(ISF), and increase the output of plumbum, an intelligent modeling method based on gray theory and artificial neural networks(ANN) is proposed, in which the weight values in the integrated model can be adjusted automatically. An intelligent predictive model of the ventilating capacity of the ISF is established and analyzed by the method. The simulation results and industrial applications demonstrate that the predictive model is close to the real plant, the relative predictive error is 0.72%, which is 50% less than the single model, leading to a notable increase of the output of plumbum.展开更多
目的基于Logistic回归和人工神经网络构建老年糖尿病足(diabetic foot,DF)患者衰弱风险预测模型,并比较两种模型预测效能,为早期识别并预防老年DF患者衰弱的发生提供依据。方法2023年5-10月,采用便利抽样法选取天津市某两所三级甲等医院...目的基于Logistic回归和人工神经网络构建老年糖尿病足(diabetic foot,DF)患者衰弱风险预测模型,并比较两种模型预测效能,为早期识别并预防老年DF患者衰弱的发生提供依据。方法2023年5-10月,采用便利抽样法选取天津市某两所三级甲等医院内491例老年DF患者为研究对象。通过问卷调查及病历记录收集资料,绘制列线图模型及人工神经网络模型;受试者工作特征曲线和曲线下面积评估模型预测能力,敏感度和特异度评估模型预测价值。结果建模组列线图和人工神经网络模型的曲线下面积(area under curve,AUC)分别为0.973、0.742,敏感度分别为92.90%、95.50%,特异度分别为91.10%、50.50%。结论构建的老年DF患者衰弱风险预测的列线图模型预测性能较好,对有效识别高衰弱风险的老年DF患者有临床价值。展开更多
文摘In order to know the ventilating capacity of imperial smelt furnace(ISF), and increase the output of plumbum, an intelligent modeling method based on gray theory and artificial neural networks(ANN) is proposed, in which the weight values in the integrated model can be adjusted automatically. An intelligent predictive model of the ventilating capacity of the ISF is established and analyzed by the method. The simulation results and industrial applications demonstrate that the predictive model is close to the real plant, the relative predictive error is 0.72%, which is 50% less than the single model, leading to a notable increase of the output of plumbum.
文摘目的基于Logistic回归和人工神经网络构建老年糖尿病足(diabetic foot,DF)患者衰弱风险预测模型,并比较两种模型预测效能,为早期识别并预防老年DF患者衰弱的发生提供依据。方法2023年5-10月,采用便利抽样法选取天津市某两所三级甲等医院内491例老年DF患者为研究对象。通过问卷调查及病历记录收集资料,绘制列线图模型及人工神经网络模型;受试者工作特征曲线和曲线下面积评估模型预测能力,敏感度和特异度评估模型预测价值。结果建模组列线图和人工神经网络模型的曲线下面积(area under curve,AUC)分别为0.973、0.742,敏感度分别为92.90%、95.50%,特异度分别为91.10%、50.50%。结论构建的老年DF患者衰弱风险预测的列线图模型预测性能较好,对有效识别高衰弱风险的老年DF患者有临床价值。
