| 摘要 |
A neuronal network is trained using measurement data of state variables comprising measurement data from input channels and measurement data from at least one output channel. The neuronal network is tested using measurement data from the input channels and measurement data from the output channel, and a first standard deviation is determined from the deviations of the predicted values for the output channel from the measurement data of the output channel. The measurement data of at least one input channel are replaced by a distribution. Values for the output channel are again calculated using the distribution or parts thereof and a second standard deviation of the calculated values for the output channel is determined from the associated measurement data. In the case of an increase in the second standard deviation compared with the first standard deviation, the input channel is significant for the neuronal network.
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