摘要
提出了一种基于快速留一交叉验证(FLOO-CV)的在线核极限学习机(OKELM),以逐次增加新样本与删除旧样本的方式进行在线训练;设计了一种无需人为设定、能够根据系统过程特性自适应改变的FLOO-CV预测误差阈值,根据误差阈值仅引入预报误差较大的样本对模型进行更新,以提高模型的稀疏性和泛化能力;利用Hermitian矩阵求逆引理实现了对网络输出权值的递推求解,减小了在线存储空间和计算时间.经混沌时间序列预测和连续搅拌釜式反应器的过程辨识结果表明,相比于离线核极限学习机、无稀疏策略的在线核极限学习机和在线序贯极限学习机,OKELM具有更快的计算速度和更高的学习精度.
A novel algorithm based on fast leave-one-out cross-validation was proposed, named as online kernel extreme learning machine （OKELM）. Online modeling was accomplished by importing the latest training sample and discarding the oldest training sample. An adaptive FLOO-CV prediction error-based threshold without any manual work was used to enhance the sparsity and generalization ability of the model by only introducing the samples with larger predictive error. The output weights of the ONELM were de- termined recursively based on Hermitian formula. Thus, the online storage space and calculation time was reduced. Numerical experiments on chaotic time series prediction and identification of a continuous stirred tank reactor show that the OKELM has faster calculation speed and higher learning accuracy in comparison with off-line kernel extreme learning machine, unsparsity online kernel extreme learning machine and on- line sequential extreme learning machine.
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2014年第5期641-646,共6页
Journal of Shanghai Jiaotong University
基金
军内科研项目资助
关键词
核方法
极限学习机
快速留一交叉验证
kernel method
extreme learning machine （ELM） ~ fast leave-one-out cross-validation （FLOO CV）
