限定记忆极端学习机及其应用

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.

Fixed-memory extreme learning machine and its applications

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).