A Topology Preserving Neural Network for Nonstationary Distributions
نویسندگان
چکیده
We propose a learning algorithm for selforganizing neural networks to form a topology preserving map from an input manifold whose topology may dynamically change. Experimental results show that the network using the proposed algorithm can rapidly adjust itself to represent the topology of nonstationary input distributions. key words: competitive Hebbian learning rule, law of the jungle mechanism, neural networks, nonstationary probability distribution, self-organizing map.
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