Geometrical synthesis of MLP neural networks
Neurocomputing
A new adaptive merging and growing algorithm for designing artificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A network pruning algorithm for combined function and derivative approximation
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A self-organizing neural network using fast training and pruning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A neural network pruning approach based on compressive sampling
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A self-organizing fuzzy neural network based on a growing-and-pruning algorithm
IEEE Transactions on Fuzzy Systems
Hidden node pruning of multilayer perceptrons based on redundancy reduction
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
A Novel Pruning Algorithm for Optimizing Feedforward Neural Network of Classification Problems
Neural Processing Letters
Advances in Artificial Neural Systems
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In this paper, we propose a new pruning algorithm to obtain the optimal number of hidden units of a single layer of a fully connected neural network (NN). The technique relies on a global sensitivity analysis of model output. The relevance of the hidden nodes is determined by analysing the Fourier decomposition of the variance of the model output. Each hidden unit is assigned a ratio (the fraction of variance which the unit accounts for) that gives their ranking. This quantitative information therefore leads to a suggestion of the most favorable units to eliminate. Experimental results suggest that the method can be seen as an effective tool available to the user in controlling the complexity in NNs.