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Adaptive resonance theory (ART)
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Evolving rule-based models: a tool for design of flexible adaptive systems
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Evolving fuzzy classifiers using different model architectures
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HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
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Information Sciences: an International Journal
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Information Sciences: an International Journal
A fast learning algorithm for evolving neo-fuzzy neuron
Applied Soft Computing
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In this paper, we extend the conventional vector quantization by incorporating a vigilance parameter, which steers the tradeoff between plasticity and stability during incremental online learning. This is motivated in the adaptive resonance theory (ART) network approach and is exploited in our paper for forming a one-pass incremental and evolving variant of vector quantization. This variant can be applied for online clustering, classification and approximation tasks with an unknown number of clusters. Additionally, two novel extensions are described: one concerns the incorporation of the sphere of influence of clusters in the vector quantization learning process by selecting the 'winning cluster' based on the distances of a data point to the surface of all clusters. Another one introduces a deletion of cluster satellites and an online split-and-merge strategy: clusters are dynamically split and merged after each incremental learning step. Both strategies prevent the algorithm to generate a wrong cluster partition due to a bad a priori setting of the most essential parameter(s). The extensions will be applied to clustering of two- and high-dimensional data, within an image classification framework and for model-based fault detection based on data-driven evolving fuzzy models.