作业帮英语翻译Ⅲ.RESULTS AND DISCUSSION3.1 The results of RSM designexp
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英语翻译
Ⅲ.RESULTS AND DISCUSSION
3.1 The results of RSM designexperiments
TABLE 2.EXPERIMENTAL
DESIGN AND RESULTS OF RSM
The extraction rate of the protein had been optimized previously using statistical
method.We optimized the extraction rate using RSM.Each extraction condition was previously using statistical
method.We optimized the rate using RSM.
Each extraction condition was
tested at three levels (-1,0,1) (table 1).The Experimental
design and the dry weight were shown in Table 2,a second-order polynomial was
established to identify the relationship between the extraction rate and three extraction factors.The maximum extraction rate by
RSM model was 0.367 g/2g DDGS.
Ⅲ.结果与讨论
3.1 RSM设计实验的结果
的蛋白质的提取率被优化了以前使用的统计方法.我们使用RSM优化的提取率.每个提取条件以前使用的统计方法.我们使用RSM优化的速度.每个提取条件进行了测试在三个层次（-1,0,1）（表1）.表2中所示,一个二阶多项式成立,以确定的提取率之间的关系,和三种提取因子的实验设计,以干基重量.RSM模型的最大提取率分别为0.367 g/2g DDGS.
3.2 ANN-Based Modelingof Protein Extraction
Two of the 15 runs in the RSM design
experiments were randomly chosen and used as prediction set and the test set,
respectively.The other experimental data were used as calibration set.A three
layers feed back artificial neural network (ANN) model was established.The
suitable number of hidden nodes of ANN model was selected depend on Da,and the
results were shown in Fig.1.According to (1),it was obvious that the larger
Da was,the more ANN models approached the experimental data.As can be seen,
Da achieved the maximum value when there were 12 hidden nodes; so the number of
hidden nodes was set to 12.
3.2基于人工神经网络的建模蛋白提取
RSM设计实验15运行在两个随机选择的,并作为预测套组和测试集,分别.的其他的实验数据被用来作为校准集.人工神经网络（ANN）模型,建立了一个三层反馈.被选择的合适数目的隐藏节点ANN模型取决于沓,其结果示于图.1.（1）,很明显,较大的大,更多的人工神经网络模型的实验数据接近.从图中可以看出,大达到最大值时有12个隐层节点数,隐层节点数设置为12.
Ⅲ.RESULTS AND DISCUSSION
3.1 The results of RSM designexperiments
TABLE 2.EXPERIMENTAL
DESIGN AND RESULTS OF RSM
The extraction rate of the protein had been optimized previously using statistical
method.We optimized the extraction rate using RSM.Each extraction condition was previously using statistical
method.We optimized the rate using RSM.
Each extraction condition was
tested at three levels (-1,0,1) (table 1).The Experimental
design and the dry weight were shown in Table 2,a second-order polynomial was
established to identify the relationship between the extraction rate and three extraction factors.The maximum extraction rate by
RSM model was 0.367 g/2g DDGS.
Ⅲ.结果与讨论
3.1 RSM设计实验的结果
的蛋白质的提取率被优化了以前使用的统计方法.我们使用RSM优化的提取率.每个提取条件以前使用的统计方法.我们使用RSM优化的速度.每个提取条件进行了测试在三个层次（-1,0,1）（表1）.表2中所示,一个二阶多项式成立,以确定的提取率之间的关系,和三种提取因子的实验设计,以干基重量.RSM模型的最大提取率分别为0.367 g/2g DDGS.
3.2 ANN-Based Modelingof Protein Extraction
Two of the 15 runs in the RSM design
experiments were randomly chosen and used as prediction set and the test set,
respectively.The other experimental data were used as calibration set.A three
layers feed back artificial neural network (ANN) model was established.The
suitable number of hidden nodes of ANN model was selected depend on Da,and the
results were shown in Fig.1.According to (1),it was obvious that the larger
Da was,the more ANN models approached the experimental data.As can be seen,
Da achieved the maximum value when there were 12 hidden nodes; so the number of
hidden nodes was set to 12.
3.2基于人工神经网络的建模蛋白提取
RSM设计实验15运行在两个随机选择的,并作为预测套组和测试集,分别.的其他的实验数据被用来作为校准集.人工神经网络（ANN）模型,建立了一个三层反馈.被选择的合适数目的隐藏节点ANN模型取决于沓,其结果示于图.1.（1）,很明显,较大的大,更多的人工神经网络模型的实验数据接近.从图中可以看出,大达到最大值时有12个隐层节点数,隐层节点数设置为12.
你网上翻译还不如不翻译呢, 这哪是叫人校对啊,还是全部重翻.呵呵
如下： 自己结合专业知识再适当修改吧.
Ⅲ.结果与讨论
3.1 RSM设计实验的结果
先使用统计方法优化了蛋白提取率,我们是采用的是RSM理念来优化的. 每一个提取条件都先用统计方法做了优化,同样采用的是RSM的理念.
每一个提取条件分别在设定的三个标准下面测试（-1,0,1）（见表一）实验设计和干重如表二所示,我们设立了一个二阶多项式以识别提取率和三个提取因素之间的关系.在RSM理念模型下的最低提取率为0.367 g/2g DDGS.
3.2基于人工神经网络（ANN-Based ）模型的蛋白提取
在RSM设计实验运行的15组中随机抽取了2个,分别作为对照集和测试集. 其他实验数据作为校准集. 以此建立一个三重反馈神经网络的模型（人工神经网络）对于神经网络模型隐藏节点合适数目的选取取决于变体,结果显示在图一中. 1. 根据图一的结果, 很明显,变体越大,越多ANN （人工神经网络）模型接近实验数据. 由此可以看出,当隐藏节点数为12的时候,变体达到最大值； 因此隐藏节点数目设定为12.
再问： 拜谢拜谢啊~~ 帮我看看这个吧，，%>_
如下： 自己结合专业知识再适当修改吧.
Ⅲ.结果与讨论
3.1 RSM设计实验的结果
先使用统计方法优化了蛋白提取率,我们是采用的是RSM理念来优化的. 每一个提取条件都先用统计方法做了优化,同样采用的是RSM的理念.
每一个提取条件分别在设定的三个标准下面测试（-1,0,1）（见表一）实验设计和干重如表二所示,我们设立了一个二阶多项式以识别提取率和三个提取因素之间的关系.在RSM理念模型下的最低提取率为0.367 g/2g DDGS.
3.2基于人工神经网络（ANN-Based ）模型的蛋白提取
在RSM设计实验运行的15组中随机抽取了2个,分别作为对照集和测试集. 其他实验数据作为校准集. 以此建立一个三重反馈神经网络的模型（人工神经网络）对于神经网络模型隐藏节点合适数目的选取取决于变体,结果显示在图一中. 1. 根据图一的结果, 很明显,变体越大,越多ANN （人工神经网络）模型接近实验数据. 由此可以看出,当隐藏节点数为12的时候,变体达到最大值； 因此隐藏节点数目设定为12.
再问： 拜谢拜谢啊~~ 帮我看看这个吧，，%>_