文中介绍了季节性神经网络建立的残差修正模型。
The paper introduces a modified model of seasonal artificial neural network.
提出了基于改进的BP神经网络学习算法和自适应残差补偿算法的炼铜转炉吹炼终点组合预报模型。
It is the first time that a converting furnace endpoint prediction model based on an improved BP neural network and error compensation of linear regression.
我们提出了一种减轻网络训练负担的残差学习框架，这种网络比以前使用过的网络本质上层次更深。
We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously.
然后利用ART （自适应共振理论）网络对残差进行自动分类，不需要故障的先验知识。
Second, ART(Adaptive Resonance Theory)is adopted to classify the residuals automatically, no apriori knowledge is required.
系统辨识是基于免疫RBF神经网络，用于故障检测的残差是通过对系统的模型输出与系统的实际输出的在线比较得到的。
The system identification is based on immune strategy RBFNN, and the residuals are generated by on-line comparing the system model outputs with the actual system outputs.
系统辨识基于免疫r BF神经网络，用于故障检测的残差是通过对系统的模型输出与系统的实际输出进行在线比较得到的。
The system identification was based on immune strategy RBFNN, and the residuals were generated by on-line comparing the system model outputs with the actual system ones.
系统辨识基于免疫r BF神经网络，用于故障检测的残差是通过对系统的模型输出与系统的实际输出进行在线比较得到的。
The system identification was based on immune strategy RBFNN, and the residuals were generated by on-line comparing the system model outputs with the actual system ones.
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