自动化学报Issue(5)：1042-1046,5.DOI:10.16383/j.aas.2015.c140604
多元混沌时间序列的因子回声状态网络预测模型
Factor Echo State Network for Multivariate Chaotic Time Series Prediction
摘要
Abstract
When an echo state network is used to predict mul-tivariate time series, there may exist ill-posed problem. This pa-per proposes a novel prediction model, named factor echo state network, to solve the problem. It uses a factor analysis (FA) al-gorithm to extract the common factors of the reservoir matrix, and to remove the redundancies and noises. Then the unknown output weights are calculated by linear regression of the output and common factors. The model is used to predict Lorenz series and monthly average temperature-rainfall time series in Dalian, and simulation results substantiate its effectiveness.关键词
多元混沌时间序列/预测/回声状态网络/因子分析Key words
Multivariate chaotic time series/prediction/echo state network/factor analysis (FA)引用本文复制引用
许美玲,韩敏..多元混沌时间序列的因子回声状态网络预测模型[J].自动化学报,2015,(5):1042-1046,5.基金项目
国家自然科学基金(61374154),国家重点基础研究发展计划(973计划)(2013CB430403)资助Supported by National Natural Science Foundation of China (61374154), and National Basic Research Program of China (973 Program)(2013CB430403) （61374154）
