Abstract
Some models using self-organization systems of neural networks are proposed in recent studies. These models show good results in point of the number of fuzzy rules in high dimensional problems. However, most of these models determine a distribution of initial fuzzy rules by considering only input data. In this paper, we propose a method considering not only input data but also output data. In order to demonstrate the validity of the proposed method, some numerical examples are performed.
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© 1998 Springer-Verlag London
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Kishida, K., Miyajima, H. (1998). A Learning Method of Fuzzy Inference Rules Using Vector Quantization. In: Niklasson, L., Bodén, M., Ziemke, T. (eds) ICANN 98. ICANN 1998. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-1599-1_128
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DOI: https://doi.org/10.1007/978-1-4471-1599-1_128
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