基于模糊分组和监督聚类的RBF回归性能改进
doi: 10.3724/SP.J.1146.2008.00350
Improved RBF Regression Using Fuzzy Partition and Supervised Fuzzy Clustering
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摘要: 为了提高RBF回归建模的精度，该文提出了一种基于模糊分组和监督聚类的RBF回归建模的新方法。基本思想是：首先利用监督聚类将训练样本模糊划分为若干子集，然后分别针对各个子集的样本分布情况进行RBF回归建模，最后利用加权组合得到最终的输出。实验表明，该方法对于目标模型的局部细节具有更好的逼近精度。Abstract: In order to improve the precision of RBF regression, this article advances a novel RBF regression modeling method using fuzzy partition and supervised clustering. The proposed method first splits the training data into several subsets using supervised clustering. Then local regression models are independently built with RBF network for each subset. Finally, the output of the network is formed with a weighted combination of each local model. Experiments show that the proposed method achieves more accurate interpretation of local behavior of the target model.
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