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rbf插值
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     A Space Interpolation Method Based on RBF Artificial Neural Network
     基于RBF人工神经网络的空间插值
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     MCG Interpolation Based on RBF ANN
     基于RBF神经网络的心磁图插值问题
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     HERMITE-BIRKHOFF INTERPOLATION OF A POLYNOMIAL
     多项式的H-B插值
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     RLH’s Interpolation
     RLH插值
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     We built the model of RBF neural network and observed detection effects through the simulation.
     构建了RBF神经网络模型.
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  rbf interpolation
Solving large radial basis function (RBF) interpolation problems with non‐customised methods is computationally expensive and the matrices that occur are typically badly conditioned.
      
Radial basis function (RBF) interpolation can be very effective for scattered data in any number of dimensions.
      
All pixels that fall within the segmented areas are then used as the inputs to the RBF interpolation.
      
For any point in the attribute space, a new shape is derived through RBF interpolation of example shapes.
      
In addition, the actual solution of the RBF interpolation problem can be prohibitive for large N, unless fast methods are implemented.
      
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In this paper, we proposed the monitoring of chemical processes with a two-stage RBF neural network The first stage is called the prediction network, which predicts the future state The second stage is called the fault diagnosis network, which checks the prediction result to identify whether there will be any faults and diagnoses the category of the faults identified In this way the monitoring of the process can be completed To improve the performance of a RBF neural network, we gave out a theorem...

In this paper, we proposed the monitoring of chemical processes with a two-stage RBF neural network The first stage is called the prediction network, which predicts the future state The second stage is called the fault diagnosis network, which checks the prediction result to identify whether there will be any faults and diagnoses the category of the faults identified In this way the monitoring of the process can be completed To improve the performance of a RBF neural network, we gave out a theorem and a revisionary method about RBF interpreation Then we proposed the other two methods in this paper They are used to improve the output of the two RBF neural networks respectively, by which the sample numbers of the two networks can be grately reduced We used the two-stage RBF neural network to the monitoring the simulation of start process of the debutanizing column in the hydrocracking fracnation section, which showed a great success

文章提出用二级RBF神经网络来实现化工过程的动态监控。第一级RBF神经网络称作预测网络,用于预测未来一段时间内的有关状态量。第二级RBF神经网络称作诊断网络,它根据预测量判断它们是否相应于某种事故状态,如果是,则提示操作人员采取相应的措施。这样可起到防患于未然的作用。RBF神经网络的基础是RBF插值,文章提出了改进的RBF插值方法。文章又提出了提前一个时间步进行预测以获得预测结果修正项的方法。另外,文章还提出对诊断网络输出结果进行变换使能合理反映事故可能性的方法。文章所述方法被用于加氢裂化脱丁烷塔开工过程的动态监控,取得了满意的结果。

 
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