Hierarchical extreme learning machine for feedforward neural network

Abstract

An approach, named extended extreme learning machine (ELM), is proposed for training the weights of a class of hierarchical feedforward neural network (HFNN). Unlike conventional single-hidden-layer feedforward networks (SLFNs), this hierarchical ELM (HELM) is based on the hierarchical structure which is capable of hierarchical learning of sequential information online, and one may simply choose hidden layers and then only need to adjust the output weights linking the hidden layer and the output layer. In such HELM implementations, the extended ELM provides better generalization performance during the learning process. Moreover, the proposed extended ELM method is efficient not only for HFNNs with sigmoid hidden nodes but also for HFNNs with radial basis function (RBF) hidden nodes. Finally, the HELM is applied to the activated sludge wastewater treatment processes (WWTPs) for predicting the water qualities. Experimental results and the performance comparison demonstrate the effectiveness of the proposed HELM.