A quantified sensitivity measure for multilayer perceptron to input perturbation

Authors:
Xiaoqin Zeng;Daniel S. Yeung
Affiliations:
Department of Computing, Hong Kong Polytechnic University, Hong Kong;Department of Computing, Hong Kong Polytechnic University, Hong Kong
Venue:
Neural Computation
Year:
2003

Citing 5
Cited 10

Output sensitivity of mlps derived from statistical expectation

Output sensitivity of mlps derived from statistical expectation
Sensitivity analysis of neocognitron

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Sensitivity analysis of multilayer perceptron to input and weight perturbations

IEEE Transactions on Neural Networks
Using function approximation to analyze the sensitivity of MLP with antisymmetric squashing activation function

IEEE Transactions on Neural Networks
The selection of weight accuracies for Madalines

IEEE Transactions on Neural Networks

Sensitivity analysis applied to the construction of radial basis function networks

Neural Networks
Computation of Madalines' Sensitivity to Input and Weight Perturbations

Neural Computation
A Novel Ensemble Approach for Improving Generalization Ability of Neural Networks

IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
A novel pruning algorithm for self-organizing neural network

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Hidden neuron pruning of multilayer perceptrons using a quantified sensitivity measure

Neurocomputing
Quantitative measurement for fuzzy system to input and rule perturbations

ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Computing and analyzing the sensitivity of MLP due to the errors of the i.i.d. inputs and weights based on CLT

IEEE Transactions on Neural Networks
Sensitivity analysis of madalines to weight perturbation

ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
One-to-many neural network mapping techniques for face image synthesis

Expert Systems with Applications: An International Journal
Computation of multilayer perceptron sensitivity to input perturbation

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The sensitivity of a neural network's output to its input perturbation is an important issue with both theoretical and practical values. In this article, we propose an approach to quantify the sensitivity of the most popular and general feedforward network: multilayer perceptron (MLP). The sensitivity measure is defined as the mathematical expectation of output deviation due to expected input deviation with respect to overall input patterns in a continuous interval. Based on the structural characteristics of the MLP, a bottom-up approach is adopted. A single neuron is considered first, and algorithms with approximately derived analytical expressions that are functions of expected input deviation are given for the computation of its sensitivity. Then another algorithm is given to compute the sensitivity of the entire MLP network. Computer simulations are used to verify the derived theoretical formulas. The agreement between theoretical and experimental results is quite good. The sensitivity measure can be used to evaluate the MLP's performance.