Abstract
The application of signal flow graphs to the learning process of neural networks is presented. By introducing the so-called adjoint graph, new insight into the mechanism of learning phenomena of the weights in neural networks has been obtained. The derived updating formulas are valid for both feedforward and recurrent neural networks and are especially useful from the hardware implementation point of view of the self-learning networks. The presented numerical experiments confirmed the usefulness of the presented approach.
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