Principles of Neurocomputing for Science and Engineering
Principles of Neurocomputing for Science and Engineering
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Sales forecasting using extreme learning machine with applications in fashion retailing
Decision Support Systems
Neural Networks and Computing: Learning Algorithms and Applications
Neural Networks and Computing: Learning Algorithms and Applications
Error minimized extreme learning machine with growth of hidden nodes and incremental learning
IEEE Transactions on Neural Networks
Rapid and brief communication: Evolutionary extreme learning machine
Pattern Recognition
A self-organizing fuzzy neural network based on a growing-and-pruning algorithm
IEEE Transactions on Fuzzy Systems
Adaptive dissolved oxygen control based on dynamic structure neural network
Applied Soft Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
BELM: Bayesian Extreme Learning Machine
IEEE Transactions on Neural Networks
Dynamic ensemble extreme learning machine based on sample entropy
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Extreme Learning Machines (ELM 2011) Hangzhou, China, December 6 – 8, 2011
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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.