摘要
为了实现极端学习机(ELM)的在线训练,提出一种限定记忆极端学习机(FM-ELM).FM-ELM以逐次增加新训练样本与删除旧训练样本的方式,提高其对于系统动态变化特性的自适应性,并根据矩阵求逆引理实现了网络输出权值的递推求解,减小了在线训练过程的计算代价.应用于具有动态变化特性的非线性系统在线状态预测表明,FM-ELM是一种有效的ELM在线训练模式,相比于在线贯序极端学习机,FM-ELM具有更快的调节速度和更高的预测精度.
To solve the problem of extreme learning machine（ELM） on-line training, an algorithm, fixed-memory extreme learning machine（FM-ELM）, is proposed. FM-ELM adopts the latest training sample and abandons the oldest training sample iteratively to enhance its adaptive capacity. The output weights of FM-ELM are determined recursively based on Sherman-Morrison formula. Thus, the computational cost of FM-ELM training procedure is effectively reduced. Numerical experiments on nonlinear system on-line condition prediction show that FM-ELM has better performance in adjusting speed and prediction accuracy in comparison with on-line sequential extreme learning machine（OS-ELM） .
出处
《控制与决策》
EI
CSCD
北大核心
2012年第8期1206-1210,共5页
Control and Decision
基金
国家部委预先研究基金项目(51309060302)
关键词
神经网络
极端学习机
在线训练
非线性系统
neural networks
extreme learning machine
on-line training
nonlinear systems
