物理学报Issue(24)：240509-1-240509-8,8.DOI:10.7498/aps.62.240509
具有增加删除机制的正则化极端学习机的混沌时间序列预测
Chaotic time series prediction using add-delete mechanism based regularized extreme learning machine
摘要
Abstract
Considering a regularized extreme learning machine (RELM) with randomly generated hidden nodes, an add-delete mechanism is proposed to determine the number of hidden nodes adaptively, where the extent of contribution to the objective function of RELM is treated as the criterion for judging each hidden node, that is, the large the better, and vice versa. As a result, the better hidden nodes are kept. On the contrary, the so-called worse hidden nodes are deleted. Naturally, the hidden nodes of RELM are selected optimally. In contrast to the other method only with the add mechanism, the proposed one has some advantages in the number of hidden nodes, generalization performance, and the real time. The experimental results on classical chaotic time series demonstrate the effectiveness and feasibility of the proposed add-delete mechanism for RELM.关键词
混沌时间序列/人工神经网络/极端学习机Key words
chaotic time series/artificial neural networks/extreme learning machine引用本文复制引用
赵永平,王康康..具有增加删除机制的正则化极端学习机的混沌时间序列预测[J].物理学报,2013,(24):240509-1-240509-8,8.基金项目
国家自然科学基金(批准号:51006052)和南京理工大学“卓越计划”“紫金之星”资助的课题.@@@@Project supported by the National Natural Science Foundation of China (Grant No.51006052) and the Outstanding Scholar Supporting Program of Nanjing Univeristy of Science and Technology, China （批准号:51006052）
