Fixed-memory extreme learning machine and its applications
Hongli Wang · Kongzhi yu juece · 2012
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).