High performance associative memory with distance based training algorithm for character recognition

The consequence of reducing the impact of the synaptic weights from neurons farther away from the neuron under consideration on a modular two-dimensional Hopfield network using Hebbian learning rule is examined for image processing applications. A generalized modular architecture is developed by defining each module with a group of neighboring neurons and all modules communicating with each other. A spatially decaying distance factor is introduced into the Hebbian rule to reduce the effect of neurons from farther modules. A biologically inspired visual perception concept has been adopted for defining the variation of the distance factor. The performance of the new technique is evaluated by conducting several experiments on character images and it is observed that the proposed method increases the learning ability and convergence rate of the network. The nature of the distance factor helps the removal of several synaptic weights farther away from a particular neuron and this leads to the reduction of complexity of the network in terms of both software and hardware implementation.