•For G(netpj) = exp[-p(netpj)2]
•Wij(new) = Wij(old) + h(tj – oj)G’(net)ai + h(tj *
net)G’(net)ai
•Using the Gaussian, and
the Rumelhart Hinton & Williams chain rule procedure, one can derive a
learning rule for value units:
•Dwij = h(dpi - epi) apj
•Essentially the same as
the gradient descent rule, with the exception of an elaborated (two
component) error term
•
•