Neural network with semi-localized non-linear mapping of the input space
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United States of America Patent
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May 12, 1992
Grant Date -
N/A
app pub date -
Jun 15, 1990
filing date -
Jun 15, 1990
priority date (Note) -
Expired
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Abstract
A neural network includes an input layer comprising a plurality of input units (24) interconnected to a hidden layer with a plurality of hidden units (26) disposed therein through an interconnection matrix (28). Each of the hidden units (26) is a single output that is connected to output units (32) in an output layer through an interconnection matrix (30). Each of the interconnections between one of the hidden units (26) to one of the output units (32) has a weight associated therewith. Each of the hidden units (26) has an activation in the i'th dimension and extending across all the other dimensions in a non-localized manner in accordance with the following equation: ##EQU1## that the network learns by the Back Propagation method to vary the output weights and the parameters of the activation function .mu..sub.hi and .sigma..sub.hi.
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