A novel pruning algorithm for self-organizing neural network

Authors:
Han Honggui;Qiao Junfei
Affiliations:
College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China;College of Electronic and Control Engineering, Beijing University of Technology, Beij ing, China
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 19
Cited 0

Optimal brain damage

Advances in neural information processing systems 2
A Simple Neural Network Pruning Algorithm with Application to Filter Synthesis

Neural Processing Letters
A quantified sensitivity measure for multilayer perceptron to input perturbation

Neural Computation
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon

Advances in Neural Information Processing Systems 5, [NIPS Conference]
An on-line algorithm for creating self-organizing fuzzy neural networks

Neural Networks
Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms

Neural Networks
Sensitivity analysis applied to the construction of radial basis function networks

Neural Networks
Fast Constructive-Covering Algorithm for neural networks and its implement in classification

Applied Soft Computing
Structure learning by pruning in independent component analysis

Neurocomputing
An integrated growing-pruning method for feedforward network training

Neurocomputing
Hidden neuron pruning of multilayer perceptrons using a quantified sensitivity measure

Neurocomputing
Active Evaluation and Ranking of Multiple-Attribute Items Using Feedforward Neural Networks

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks

IEEE Transactions on Fuzzy Systems
A new pruning heuristic based on variance analysis of sensitivity information

IEEE Transactions on Neural Networks
Pruning error minimization in least squares support vector machines

IEEE Transactions on Neural Networks
A constructive algorithm for training cooperative neural network ensembles

IEEE Transactions on Neural Networks
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation

IEEE Transactions on Neural Networks
Constructive feedforward neural networks using Hermite polynomial activation functions

IEEE Transactions on Neural Networks
Comparing Support Vector Machines and Feedforward Neural Networks With Similar Hidden-Layer Weights

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a novel pruning algorithm is proposed for self-organizing the feed-forward neural network based on the sensitivity analysis, named novel pruning feed-forward neural network (NP-FNN). In this study, the number of hidden neurons is determined by the output's sensitivity to the hidden nodes. This technique determines the relevance of the hidden nodes by analyzing the Fourier decomposition of the variance. Then each hidden node can obtain a contribution ratio. The connected weights of the hidden nodes with small ratio will be set as zeros. Therefore, the computational cost of the training process will be reduced significantly. It is clearly shown that the novel pruning algorithm minimizes the complexity of the final feed-forward neural network. Finally, computer simulation results are carried out to demonstrate the effectiveness of the proposed algorithm.