A Learning Method of Fuzzy Inference Rules Using Vector Quantization
A Learning Method of Fuzzy Inference Rules Using Vector Quantization
复制标题
一种利用矢量量化的模糊推理规则学习方法
DOI：
10.1007/978-1-4471-1599-1_128
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发表时间：
1998
期刊：
影响因子：
--
通讯作者：
H. Miyajima
中科院分区：
文献类型：
--
作者：
K. Kishida;H. Miyajima
近年来的研究提出了一些利用神经网络自组织系统的模型。这些模型在高维问题的模糊规则数量方面显示出良好的效果。然而，这些模型大多只考虑输入数据来确定初始模糊规则的分布。本文提出了一种既考虑输入数据又考虑输出数据的方法。为了验证该方法的有效性，给出了数值算例。
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.