fuzzy input to Neural network

From: MG (learnts@yahoo.com)
Date: Wed Dec 19 2001 - 09:09:45 MET

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    I am exploring the possibility of using fuzzy inputs to neural
    network.
    The output of NN model depends on some *quantitative* as well as some
    *qualitative* data. The qualitative data can be roughly approximated
    by using some boolean logic.
    e.g. excellent= 1: when the data is in the range 81-100
         very good=1: when the data is in the range 61-80
         good=1: when the data is in the range 41-60
         bad=1: when the data is in the ragne 0-40

      
    I have thought of one possible use of boolean logic as input into the
    network. The inputs of NN model are then clear to me : consists of
    some *qunatitative* variables and some *boolean* variable.
    e.g. with 6 inputs(4 boolean),one pattern may look like(if the
    *qualitative* data has a numerical value of 81)
            INPUTS OUTPUT
    1 2 3 4 5 6 1
    0.78 0.59 1 0 0 0 0.69
        

    But clearly the classification boundaries here are very fuzzy. To
    assign '1' as excellent when the input data has a value of '81' and
    '0' when the input data has a value of '80' will not be so justful.
    That is why I am pondering to seek towards fuzzy set and fuzzy logic
    for help.
    Now,when we use fuzzy set theory, by defining membership function we
    may end up having some values in 'excellent' and some values in 'very
    good' for the same value of '81' and '0' for good and bad classes. Say
    for example we may have following inputs,
            INPUTS OUTPUT
    1 2 3 4 5 6 1
    0.78 0.59 0.9 0.3 0 0 0.69
    We may also use normalized values for input'3' and'4' and make the sum
    as 1 instead of 1.2.

    * Is this method valid? If not how can I input these values having
    fuzzy boundaries in NN model?
     If the NN model can perform well in the training and
    cross-validation/test phase then one might assume that the method
    worked. But, I wonder if my basis of work(In the framework of NN) is
    incorrect or questionable then having excellent result would have no
    meaning.
    * Are there some good sites in the web addressing coupling of fuzzy
    logic and neural network(e.g. some good tutorials to start with), or
    some good books which are good as 'self-study'materials.

    Mg

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