物理学报2011，Vol.60Issue(10)：765-772,8.
基于鲁棒回声状态网络的混沌时间序列预测研究
Chaotic time series prediction based on robust echo state network
摘要
Abstract
Focusing on the problem that the echo state network is easily influenced by outliers,in this paper we propose a robust model based on the Laplace prior distribution.This is achieved by replacing the Gaussian distribution with the Laplace distribution as the prior of the model output,the Laplace prior is less sensitive to the outliers and can enhance the capbility of the model to restrict outliers.Furthermoer,to solve the problem arising from the introduction of the Laplace distribution,which makes the solving process of the method difficlut,the bound optimization algorithm is employed and a suitable surrogate function is established.Based on the bound optimization algorithm,the Laplace prior can be equivalently transformed into the form of Gaussian prior,which is easily computed,and it can also be use to estimate the model parameters adaptively.Simulation results illustrate that the proposed method can be robust when outliers exist,while remaining acceptable prediction accuracy.关键词
回声状态网络/鲁棒模型/替代函数/拉普拉斯分布Key words
echo state network/robust model/surrogate function/Laplace distribution分类
信息技术与安全科学引用本文复制引用
李德才,韩敏..基于鲁棒回声状态网络的混沌时间序列预测研究[J].物理学报,2011,60(10):765-772,8.基金项目
国家自然科学基金（批准号：61074096）资助的课题 （）
