控制理论与应用2011，Vol.28Issue(4)：601-604,4.
基于Adaboost算法的回声状态网络预报器
Improvement of echo state network accuracy with Adaboost
摘要
Abstract
Modifying the prediction model of individual echo state network(ESN) improves the total prediction result with limited extent. To solve this probiem, we consider an ensemble of ESN. The general performance and prediction accuracy of each individual ESN is boosted by using the Adaboost algorithm. Based on the Adaboost algorithm results,we develop an ESN predictor(ABESN). In this predictor, the weights of training samples are constantly adjusted according to the fitting error, the greater the fitting error, the heavier the weights for the training samples. Therefore, the ESN predictor will focus on the hard-learning samples in the next iteration cycle. The prediction models of individual ESN are weighted and added up to form the final predictor of the ensemble of ESN. The presented model is tested on the benchmark prediction problem of Mackey-Glass time series as well as the time series of sunspots. Simulation results demonstrate its high prediction accuracy and effectiveness.关键词
ESN/Adaboost.RT算法/非线性时间序列/预测Key words
ESN/ Adaboost/ RT algorithm/ nonlinear time series/ prediction分类
信息技术与安全科学引用本文复制引用
韩敏,穆大芸..基于Adaboost算法的回声状态网络预报器[J].控制理论与应用,2011,28(4):601-604,4.基金项目
国家自然科学基金资助项目(60674073) （60674073）
国家高技术研究发展"863"计划资助项目(200AA04Z158) （200AA04Z158）
国家科技支撑计划资助项目(2006BAB14B05). （2006BAB14B05）
