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In this paper, we propose a self-growing learning vector quantization with additional learning(SGLVQ). It has high pattern recognition ability. In addition, additional learning is possible. The proposed SGLVQ is constructed based on self-organizing map (SOM) and learning vector quantization (LVQ) and is composed of 2 layers: the input/output layer and the rule layer. There are full connections between the input/output layer and the rule layer. Learning of the SGLVQ consists of 2 steps: SOM step and LVQ step.In the SOM step, similar inputs are mapped in the neighboring area using SUM algorithm. In the LVQ step, neurons are incremented and size of the network is adjusted automatically. Neurons increased when mis-recognition oc curs.The increment of neurons enables additional learning and contributes to obtain high recognition ability.

Self-growing Learning Vector Quantization with Additional Learning Ability