基于相容粗糙集的ELM网络结构选择

ELM network architecture selection based on tolerance rough set technique

结构选择是神经网络研究的热点,文章提出了一种基于相容粗糙集技术的ELM(Extreme LearningMachine)网络结构选择方法,给定一个含有很多隐含层结点的前馈神经网络。该方法用相容依赖度度量隐含层结点的重要性,将不重要的隐含层结点逐一去掉,直到满足预定义的终止条件为止。实验结果表明,该文提出的方法是行之有效的。

Architecture selection is hot topic in the study of neural networks. Based on tolerance rough set technique, a method of architecture selection for ELM network is proposed in this paper. Given a single layered forward neural network with many hidden nodes, the method employs the concept of tolerance dependency to measure the significance of hidden nodes and keeps removing the nodes which are not of great importance until the predefined termination condition is satisfied. The experimental results show that the proposed method is effective.