Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Self-Organizing Maps
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
Fast self-organizing feature map algorithm
IEEE Transactions on Neural Networks
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Genetic algorithm is introduced to network optimization to overcome the limitation of conventional SOM network. Based on this idea, a new model of structural adapting self-organizing neural network is proposed. In this model, each neuron is regarded as individual of evolutionary population and three operators are constructed as follows:growing operator, pruning operator and stochastic creating operator. In the algorithm, the accumulative error of neuron is selected as fitness function each iteration, and the neurons on compete layer are generated or deleted adaptively according to the values of fitness function until there is not any change of neuron on compete layer. Simulation experiments indicate that this structural adaptive network has better performance than conventional SOM network.