On the stationary state of Kohonen's self-organizing sensory mapping
Biological Cybernetics
The 'Neural' Phonetic Typewriter
Computer
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Topology-conserving maps for learning visuo-motor-coordination
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
The effect of concave and convex weight adjustments on self-organizing maps
IEEE Transactions on Neural Networks
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This paper proposes an escape methodology to the local minima problem of self organizing feature maps generated in the overlapping regions which are equidistant to the corresponding winners. Two new versions of the Self Organizing Feature Map are derived equipped with such a methodology. The first approach introduces an excitation term, which increases the convergence speed and efficiency of the algorithm, while increasingthe probability of escaping from local minima. In the second approach, we associate a learning set which specifies the attractive and repulsive field of output neurons. Results indicate that accuracy percentile of the new methods are higher than the original algorithm while they have the ability to escape from local minima.