Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Self-organizing maps
Pattern Recognition by Self-Organizing Neural Networks
Pattern Recognition by Self-Organizing Neural Networks
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Kohonen Feature Map Associative Memory with Refractoriness based on Area Representation
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Variable-Sized kohonen feature map probabilistic associative memory
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
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In this paper, we propose a Kohonen Feature Map associative memory with area representation which can learn patterns successively. This model is based on the Kohonen Feature Map associative memory and the area representation. Most of the conventional models which can learn patterns successively are based on the associative memories, and their storage capacities are small because their learning algorithm is based on the Hebbian learning. On the other hands, the Kohonen Feature Map associative memory based on the local representation has been proposed. It has large storage capacity, but it has not enough robustness for damaged neurons. In the proposed model, the area representation which is an intermediate representation of the local representation and the distributed representation is introduced and the robustness for damaged neurons is improved. Moreover, the proposed model can realize auto and hetero associations and can deal with not only binary or bipolar patterns but also analog patterns. We carried out a series of computer experiments and confirmed the effectiveness of the proposed model.