Appeal No. 96-0065
Application No. 07/969,868
The invention is directed to a method and apparatus for
neural network training.
Representative independent claim 1 is reproduced as
follows:
1. A method of training a neural network so that a
neuron output state vector thereof obeys a set of forward
sensitivity equations over a finite learning period, said
method comprising:
defining first and auxiliary adjoint systems of equations
governing an adjoint function and an auxiliary adjoint
function, respectively, of said neural network;
setting said adjoint function to zero at the beginning of
said learning period and integrating said adjoint system of
equations forward in time over said learning period to produce
a first term of an indirect effect of a sensitivity gradient
of said neural network;
setting said auxiliary adjoint function to zero at the
end of said learning period and integrating said auxiliary
adjoint system of equations forward in time over said learning
period to produce a remaining term of said indirect effect;
computing a sum of said first and remaining terms, and
multiplying said sum by a learning rate; and
subtracting the product thereof from a current neuron
parameter vector to produce an updated neuron parameter
vector.
No references are relied on by the examiner.
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