Neural Computation
January 1993, Vol. 5, No. 1, Pages 154-164
(doi: 10.1162/neco.1993.5.1.154)
Learning in the Recurrent Random Neural Network
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Abstract
The capacity to learn from examples is one of the most desirable features of neural network models. We present a learning algorithm for the recurrent random network model (Gelenbe 1989, 1990) using gradient descent of a quadratic error function. The analytical properties of the model lead to a "backpropagation" type algorithm that requires the solution of a system of n linear and n nonlinear equations each time the n-neuron network "learns" a new input-output pair.