Generalizing Operations of Binary Autoassociative Morphological Memories Using Fuzzy Set Theory
Journal of Mathematical Imaging and Vision
Associative morphological memories based on variations of the kernel and dual kernel methods
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
A Design Method of Associative Memory Model with Expecting Fault-Tolerant Field
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Suitability of two associative memory neural networks to character recognition
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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Associative memories (AMs) can be implemented using networks with or without feedback. We utilize a two-layer feedforward neural network and propose a learning algorithm that efficiently implements the association rule of a bipolar AM. The hidden layer of the network employs p neurons where p is the number of prototype patterns. In the first layer, the input pattern activates at most one hidden layer neuron or “winner”. In the second layer, the “winner” associates the input pattern to the corresponding prototype pattern. The underlying association principle is minimum Hamming distance and the proposed scheme can be viewed also as an approximately minimum Hamming distance decoder. Theoretical analysis supported by simulations indicates that, in comparison with other suboptimum minimum Hamming distance association schemes, the proposed structure exhibits the following favorable characteristics: 1) it operates in one-shot which implies no convergence-time requirements; 2) it does not require any feedback; and 3) our case studies show that it exhibits superior performance to the popular linear system in a saturated mode. The network also exhibits 4) exponential capacity and 5) easy performance assessment (no asymptotic analysis is necessary). Finally, since it does not require any hidden layer interconnections or tree-search operations, it exhibits low structural as well as operational complexity