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中文摘要：
径向基函数极限学习机(radial basis function-extreme learning machine,RBF-ELM)中的两个参数都随机地生成,这导致RBF-ELM算法的不稳定性问题。另外,对于不同的数据集,难于确定隐含层结点的个数。针对RBFELM的这两个问题,提出了一种改进算法。首先用核心集方法选择重要的样例,然后用选择的样例初始化中心参数,宽度参数采用随机化方法初始化。该算法不仅可以在一定程度上解决RBF-ELM的不稳定性问题,而且可以确定隐含层结点的个数。试验结果表明:该算法优于RBF-ELM。
英文摘要：
Radial basis function-extreme learning machine (RBF-ELM)employed randomized method to initialize the centers and widths.Randomly initialization of the two parameters led to instability of RBF-ELM.Moreover,for differ-ent data sets,it was difficult to determine the number of the hidden nodes.An improved algorithm was proposed,which firstly selected important instances with core set method,and then the centers were initialized with the selected in-stances,the width parameters were randomly initialized.The proposed algorithm not only could solve the problem of the instability of RBF-ELM to some extent,but also could determine the number of hidden layer nodes.Experimental results showed that the proposed algorithm outperforms RBF-ELM algorithm.
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