Improvement and application of extreme learning machine algorithm
Niu Pei-fen · Journal of Yanshan University · 2015
Extreme learning machine is a novel single hidden layer feed-forward neural network model,whose input weights and the bias of hidden nodes are generated randomly. And its output weights are computed analytically. Consequently,the extreme learning machine owns extremely fast speed and good identification ability,which is faster than conventional BP neural network thousands times. However,the stochastic input weights and the bias of the extreme learning machine are not the best model parameters possibly when the objective function gets the global minimum value. Therefore,the least square method is adopted to seek the appropriate parameters of extreme learning machine. The improved extreme learning machine is applied to build the combustion thermal efficiency model of the plant boiler. Compared with other algorithms,such as BP,conventional extreme learning machine,particle swarm optimization extreme learning machine,teaching-learning-based optimization extreme learning machine,the result shows that the improved extreme learning machine is effective.