Computation of multilayer perceptron sensitivity to input perturbation

Authors:
Jing Yang;Xiaoqin Zeng;Shuiming Zhong
Affiliations:
Institute of Intelligence Science and Technology, Hohai University, Nanjing 210098, China;Institute of Intelligence Science and Technology, Hohai University, Nanjing 210098, China;Institute of Intelligence Science and Technology, Hohai University, Nanjing 210098, China
Venue:
Neurocomputing
Year:
2013

Citing 19
Cited 0

Multilayer feedforward networks are universal approximators

Neural Networks
A quantified sensitivity measure for multilayer perceptron to input perturbation

Neural Computation
A Quantitative Study of Fault Tolerance, Noise Immunity, and Generalization Ability of MLPs

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

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

Neural Computation
Image classification with the use of radial basis function neural networks and the minimization of the localized generalization error

Pattern Recognition
Feature selection using localized generalization error for supervised classification problems using RBFNN

Pattern Recognition
Radial Basis Function network learning using localized generalization error bound

Information Sciences: an International Journal
A sensitivity-based approach for pruning architecture of Madalines

Neural Computing and Applications
Hidden neuron pruning of multilayer perceptrons using a quantified sensitivity measure

Neurocomputing
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
A new pruning heuristic based on variance analysis of sensitivity information

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

IEEE Transactions on Neural Networks
Sensitivity of feedforward neural networks to weight errors

IEEE Transactions on Neural Networks
Computation of Adalines' sensitivity to weight perturbation

IEEE Transactions on Neural Networks
Sensitivity Analysis of the Split-Complex Valued Multilayer Perceptron Due to the Errors of the i.i.d. Inputs and Weights

IEEE Transactions on Neural Networks
Localized Generalization Error Model and Its Application to Architecture Selection for Radial Basis Function Neural Network

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

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

The sensitivity of a neural network's output to its inputs' perturbations is an important measure for evaluating the network's performance. To make the sensitivity be a practical tool for designing and implementing Multilayer Perceptrons (MLPs), this paper proposes a general approach to quantify the sensitivity of MLPs. The sensitivity is defined as the mathematical expectation of absolute output deviations due to input perturbations with respect to all possible inputs, and computed following a bottom-up way, in which the sensitivity of a neuron is first considered and then is that of the entire network. The main contribution of the approach is that it requests a weak assumption on the input, that is, input elements need only to be independent of each other without being restricted to have a certain type of distribution and thus is more applicable to real applications. Some experimental results on artificial datasets and real datasets demonstrate the proposed approach is highly accurate.