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Learning method for a neural network

  • US 5,748,848 A
  • Filed: 08/19/1996
  • Issued: 05/05/1998
  • Est. Priority Date: 08/21/1995
  • Status: Expired due to Term
First Claim
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1. A learning method for training a recurrent neural network having a plurality of inputs and a plurality of outputs and at least one return line connecting an output to an input, comprising the steps of:

  • a) separating said at least one return line during training of the neural network and using the input connected to said return line as a training input together with the other inputs;

    b) in a computer, interpreting input quantities supplied to the inputs of said neural network for training as a time series of a set of values of a variable input quantity representing respective values of the input quantity at discrete points in time;

    c) in said computer identifying a statistical noise distribution of an uncorrelated noise of finite variance that has a chronological average of zero and is superimposed on the measured values;

    d) in said computer generating a respective inputs values for any additional training inputs by, for each input value for each additional training input, treating the input value as a missing value in said time series, calculating a statistical missing value noise distribution according to said known noise distribution from at least one of said input quantity values neighboring the missing value in the time series and calculating said value of the missing value by replacing the missing value with at least two Monte Carlo samples of the missing value obtained according to the missing value noise distribution; and

    e) training said neural network using said time series and a behavior of a technical system represented by the neural network.

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