Data normalization in nonstationary data classification with Learn++.NSE based on MLP

I need to predict technical aggregate condition using vibration monitoring data. We consider this data to be nonstationary i.e. distribution parameters and descriptive statistics are not constant. I found that one of the best algorithms for such tasks in Learn++.NSE and we us it with MLP as a base classifier.

As I know, it's necessary no normalize data for operations with ANN. We decided to normalize using mean, stdev and sigmoidal function. We train networks of ensemble with sets with different values distribution parameters.

So, my questions are the following

  1. How to normalize new trainig set during previous networks evaluation? Problem is in description statistics change
  2. How to normalize input data while ensemble usage? Current statistics differ from the previous ones.

Topic normalization neural-network time-series

Category Data Science

1
answered at

Batch normalization is critical technique for fast learning speed and generalization [8]. In this paper, batch temporal normalization layer is proposed for stationarity of input time series.

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