关键词: 急速学习机, 多隐层神经网络, 逐层优化

Abstract: The number of hidden nodes has strong influence on the accuracy of ELM（Extreme Learning Machine）. More hidden nodes are needed as the increase of the size of training data set. Either ELM or multi-hidden layer neural network determines the number of hidden layer in advance and then increases the number of nodes in every layer to achieve a smaller RMSE. Thus the computational complexity of the involved matrix becomes bigger, leading to worse learning efficiency. In this paper extreme learning machine with incremental hidden layers is proposed, in which the weights of hidden nodes can be assigned randomly to the current hidden layer nodes（small number, don’t optimize and small complexity） and then the corresponding RMSE is obtained like ELM. MHL-ELM increases a hidden layer unless the RMSE of the network reaches the request. Besides, the complexity of MHL-ELM is[l=1MO(N3l)]. The experiment shows MHL-ELM has better generalization performance, smaller RMSE and shorter learning time, compared with some traditional algorithms like BP or OP-ELM based on ten UCI, keel data sets and real data sets.

Key words: Extreme Learning Machine（ELM）, multi-hidden layer neural network, layer-wise pre-training