中南大学学报（自然科学版）2013，Vol.44Issue(4)：1397-1402,6.
一种基于自适应粒子滤波的多层感知器学习算法
An algorithm for MLPs training based on adaptive particle filter
摘要
Abstract
To overcome the problems such as low filtering accuracy and divergence caused by unknown system noise statistics in state space neural network model estimation, an adaptive particle filter (APF) is proposed. By applying the sequential to estimate the variance of unknown system noise online, the particle filter is used to estimate the weights of the multi-layer perceptrons. The simulation results show that the APF algorithm outperforms the some conventional training algorithms such as the extended kalman filter (EKF), the EKF algorithm with evidence maximization and sequentially updated priors (EKFQ), and the general particle filter.关键词
多层感知器/粒子滤波/自适应粒子滤波Key words
multi-layer perceptrons/ particle filter/ adaptive filter分类
信息技术与安全科学引用本文复制引用
席燕辉,叶志成,彭辉..一种基于自适应粒子滤波的多层感知器学习算法[J].中南大学学报（自然科学版）,2013,44(4):1397-1402,6.基金项目
国家国际科技合作计划项目(2011DFA10440) （2011DFA10440）
国家自然科学基金委创新群体资助项目(70921001) （70921001）
国家自然科学基金资助项目(60574058) （60574058）
湖南省科技计划国际合作重点项目(2009WK2009) （2009WK2009）
湖南省教育厅项目(11C0023) （11C0023）
