As network input, the fault feature is enhanced by eigen-function in modeling.
在建模过程中,利用特征函数强化故障特征作为网络输入。
The mean accurate rate of recognition of the LVQ neural network would be different, as the input vectors comtained different kind of FTIR characteristic frequencies.
作为输入的FTIR特征谱峰不同时,则网络的平均分类识别正确率也不同。
And we suggest that mode frequencies, mode shapes and mode flexibility are regarded as input parameters of neural network modification.
提出了利用模态频率、模态振型和模态柔度组合指标作为神经网络修正的输入参数。
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