关键词: 深度神经网络, 极限学习机, 自编码, 分类

Abstract:  For the autoencoder neural networks having long training time, this paper puts forward a kind of improved deep autoencoder neural network. Firstly, this paper uses extreme learning machine (ELM) as an autoencoder block and constructs a multilayer autoencoder neural network, to improve the classification accuracy. Using ELM can avoid the iterative process and reduce the training time of network. Secondly, this paper adds the label tag in the output layer nodes, and expects the actual output with the sample tag, making the unsupervised learning to be a supervised learning and achieving the classification training in the process of deep learning. To verify the validity of the proposed method, this paper tests in the multiple UCI datasets. The experimental results show that the accuracy is good and the training speed is improved, compared with other autoencoder networks and radial basis function (RBF) neural network.

Key words: deep neural network, extreme learning machine, autoencoder, classification