Technical report on learning in recurrent networks

[Erol Gelenbe]

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Learning in the Recurrent Random Network

by

Erol Gelenbe
EHEI
45 rue des Saints-Peres
75006 Paris



This paper describes an "exact" learning algorithm for the recurrent random network
model (see E. Gelenbe in Neural Computation, Vol 2, No 2, 1990). The algorithm is
based on the delta rule for updating the network weights. Computationally, each
step requires the solution of n non-linear equations (solved in time Kn where K
is a constant) and 2n linear equations for the derivatives. Thus it is of O(n**3)
complexity, where n is the number of neurons.