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Chen, X., Zeng, X., Chu, R. and Zhong, S. (2010) A Quantified Sensitivity Measure of Radial Basis Function Neural Networks to Input Variation. IJCNN, 2010 Joint Conference on Neural Networks, Barcelona, 18-23 July 2010, 1-6.
https://doi.org/10.1109/IJCNN.2010.5596949

has been cited by the following article:

• TITLE: Sensitivity Analysis of Radial Basis Function Networks for River Stage Forecasting

  AUTHORS: Christian Walker Dawson

  KEYWORDS: Artificial Neural Networks, Backward Chaining, Multi-Layer Perceptron, Partial Derivative, Radial Basis Function, Sensitivity Analysis, River Stage Forecasting

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.13 No.12, December 23, 2020

  ABSTRACT: Sensitivity analysis of neural networks to input variation is an important research area as it goes some way to addressing the criticisms of their black-box behaviour. Such analysis of RBFNs for hydrological modelling has previously been limited to exploring perturbations to both inputs and connecting weights. In this paper, the backward chaining rule that has been used for sensitivity analysis of MLPs, is applied to RBFNs and it is shown how such analysis can provide insight into physical relationships. A trigonometric example is first presented to show the effectiveness and accuracy of this approach for first order derivatives alongside a comparison of the results with an equivalent MLP. The paper presents a real-world application in the modelling of river stage shows the importance of such approaches helping to justify and select such models.