A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Self-organizing maps
Pattern Recognition by Self-Organizing Neural Networks
Pattern Recognition by Self-Organizing Neural Networks
Graph Theory in Modern Engineering: Computer Aided Design, Optimization, Reliability Analysis
Graph Theory in Modern Engineering: Computer Aided Design, Optimization, Reliability Analysis
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The self-organizing feature map (SOFM) algorithm can be generalized, if the regular neuron grid is replaced by an undirected graph. The training rule is furthermore very simple: after a competition step, the weights of the winner neuron and its neighborhood must be updated. The update is based on the generalized adjacency of the initial graph. This feature is invariant during the training; therefore its derivation can be achieved in the preprocessing. The newly developed self-organizing neuron graph (SONG) algorithm is applied in function approximation, character fitting and satellite image analysis. The results have proven the efficiency of the algorithm.