A self-tuning method of fuzzy modeling with learning vector quantization
Kazuya Kishida, Michiharu Maeda, Hiromi Miyajima, Sadayuki Murashima · 2002
We propose a self-creating method of fuzzy modeling with learning vector quantization. A self-creating neural network is used for vector quantization. There are many fuzzy models using self-organization and vector quantization. It is well known that these models effectively construct fuzzy inference rules representing distribution of input data, and are not affected by increment of input dimensions. We use a self-creating neural network for constructing fuzzy inference rules. In order to show the validity of the proposed method, we perform some numerical examples.