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System and method for dynamic learning control in genetically enhanced back-propagation neural networks

  • US 5,832,466 A
  • Filed: 08/12/1996
  • Issued: 11/03/1998
  • Est. Priority Date: 08/12/1996
  • Status: Expired due to Fees
First Claim
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1. A method of producing an artificial neural network wherein an output response to training is dependent upon at least a parameter value and for use in a computer, the method comprising the steps of:

  • a) forming a plurality of groups of individual artificial neural networks, each individual in each group having a unique parameter value, the parameter values defining a first broad range of values, the individuals within a group being related to each other by the closeness of their parameter value;

    b) using the computer, applying to each individual a plurality of input stimuli and their corresponding expected output responses;

    c) using the computer, testing the individuals by applying to each individual at least an input stimulus and comparing at least a corresponding output response to at least a corresponding expected output response to determine the parameter value that is a best fit, wherein the best fit is assigned to the parameter value of the individual that provides a closest output response to the expected output response;

    d) using the computer, assigning new parameter values to the plurality of groups of individuals based on the parameter value that is the best fit, the new parameter values defining a second range of values; and

    ,e) repeating steps (b) to (d) until an individual in a group is within a predetermined tolerance of the at least an expected output response.

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