Universal approximation using radial-basis-function networks
Neural Computation
Technometrics
Recursive Lazy Learning for Modeling and Control
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Letters: Convex incremental extreme learning machine
Neurocomputing
OP-ELM: Theory, Experiments and a Toolbox
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Model selection approaches for non-linear system identification: a review
International Journal of Systems Science
Error minimized extreme learning machine with growth of hidden nodes and incremental learning
IEEE Transactions on Neural Networks
Multiresponse sparse regression with application to multidimensional scaling
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Fast orthogonal least squares algorithm for efficient subset modelselection
IEEE Transactions on Signal Processing
A two-stage algorithm for identification of nonlinear dynamic systems
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
Classification ability of single hidden layer feedforward neural networks
IEEE Transactions on Neural Networks
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
Learning capability and storage capacity of two-hidden-layer feedforward networks
IEEE Transactions on Neural Networks
Smooth function approximation using neural networks
IEEE Transactions on Neural Networks
Neuron selection for RBF neural network classifier based on data structure preserving criterion
IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
A Hybrid Forward Algorithm for RBF Neural Network Construction
IEEE Transactions on Neural Networks
On the performance of the µ-GA extreme learning machines in regression problems
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
A multi-objective micro genetic ELM algorithm
Neurocomputing
A study on the randomness reduction effect of extreme learning machine with ridge regression
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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Extreme learning machine (ELM) proposed by Huang et al. was developed for generalized single hidden layer feedforward networks (SLFNs) with a wide variety of hidden nodes. It proved to be very fast and effective especially for solving function approximation problems with a predetermined network structure. However, the method for determining the network structure of preliminary ELM may be tedious and may not lead to a parsimonious solution. In this paper, a systematic two-stage algorithm (named TS-ELM) is introduced to handle the problem. In the first stage, a forward recursive algorithm is applied to select the hidden nodes from the candidates randomly generated in each step and add them to the network until the stopping criterion achieves its minimum. The significance of each hidden node is then reviewed in the second stage and the insignificance ones are removed from the network, which drastically reduces the network complexity. The effectiveness of TS-ELM is verified by the empirical studies in this paper.