Computation of madalines' sensitivity to input and weight perturbations.
The sensitivity of a neural network's output to its input and weight perturbations is an important measure for evaluating the network's performance. In this letter, we propose an approach to quantify the sensitivity of Madalines. The sensitivity is defined as the probability of output deviation due to input and weight perturbations with respect to overall input patterns. Based on the structural characteristics of Madalines, a bottom-up strategy is followed, along which the sensitivity of single neurons, that is, Adalines, is considered first and then the sensitivity of the entire Madaline network. By means of probability theory, an analytical formula is derived for the calculation of Adalines' sensitivity, and an algorithm is designed for the computation of Madalines' sensitivity. Computer simulations are run to verify the effectiveness of the formula and algorithm. The simulation results are in good agreement with the theoretical results.
Wang
Yingfeng
Y
Department of Computer Science, Hohai University, Nanjing, China. ywang802@vip.sina.com
Zeng
Xiaoqin
X
Yeung
Daniel So
DS
Peng
Zhihang
Z
D000818:Animals; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity; D014894:Weights and Measures