摘要
针对采用回声状态网络预测多元混沌时间序列时储备池学习算法可能存在的病态解问题,该文提出了一种基于快速子空间分解方法的回声状态网络预测模型.所提模型利用Krylov子空间分解方法提取储备池状态矩阵的子空间,子空间代替原状态矩阵进行输出权值求解,可以消除储备池状态矩阵的冗余信息,有效地解决伪逆算法存在的病态解问题,并且降低计算复杂度,提高泛化性能和预测精度.基于两组多元混沌时间序列的仿真结果验证了该文所提模型的有效性和实用性.
Considering that there may be ill-posed solutions in the pseudo-inverse algorithm for using echo state network to predict the multivariate chaotic time series,we introduce a new approach towards ESNs,termed Fast Subspace Decomposition Echo State Network model（FSDESN）herein.The model uses a Krylov subspace decomposition algorithm,on the basis of an efficient Lanczos type iteration,to extract the subspace of large-scale reservoir matrix,and then calculate the output weights by replacing the original state space with its subspace.By this way,it brings a jump on computational complexity compared with conventional eigenvalue decomposition algorithm.In addition,FSDESN model eliminates approximate collinear components so as to solve the ill-posed problem and avoid over-training.Furthermore,it improves the generalization performance of single ESN.Simulation results on basis of two sets of multivariate chaotic time series substantiate the effectiveness and characteristics of FSDESN.
出处
《计算机学报》
EI
CSCD
北大核心
2014年第11期2268-2275,共8页
Chinese Journal of Computers
基金
国家自然科学基金(61374154
61074096)
国家"九七三"重点基础研究发展规划项目基金(2013CB430403)资助~~
关键词
回声状态网络
快速子空间分解
储备池
多元时间序列
预测
echo state network
fast subspace decomposition
reservoir
multivariate time series
prediction
