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Systems and methods for training neural networks based on concurrent use of current and recorded data

  • US 8,489,528 B2
  • Filed: 07/28/2010
  • Issued: 07/16/2013
  • Est. Priority Date: 07/28/2009
  • Status: Active Grant
First Claim
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1. A computer program product embodied in a non-transitory computer-readable medium, the computer program product comprising an algorithm adapted to effectuate a method comprising:

  • providing a neural network comprising a plurality of estimated weights for estimating a linearly parameterized uncertainty;

    receiving past data in the neural network;

    recording one or more of the past data for future use;

    receiving current data in the neural network; and

    updating the estimated weights of the neural network, with a computer processor, based on concurrent processing of the current data and the selected past data, wherein convergence of the estimated weights to ideal weights is guaranteed when the recorded past data contains as many linearly independent elements as a dimension of a basis for the uncertainty.

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