A quantified sensitivity measure of Radial Basis Function Neural Networks to input variation

The sensitivity of a neural network´s output to its parameter variation is an important issue in both theoretical researches and practical applications of neural networks. This paper proposes a quantified sensitivity measure of the Radial Basis Function Neural Networks (RBFNNs) to input variation. The sensitivity is defined as the mathematical expectation of squared output deviations caused by input variations. In order to quantify the sensitivity, the input is treated as a statistical variable and a numerical integral technique is employed to approximately compute the expectation. Experimental verifications are run and the results show a very good agreement between the proposed sensitivity computation and computer simulation. The quantified sensitivity measure could be helpful as a general tool for evaluating RBFNNs´ performance.