title: Computation of madalines' sensitivity to input and weight perturbations.
abstract: 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.
authors.affiliation: Department of Computer Science, Hohai University, Nanjing, China. ywang802@vip.sina.com
authors.forename: Yingfeng
authors.initials: Y
authors.lastname: Wang
authors.forename: Xiaoqin
authors.initials: X
authors.lastname: Zeng
authors.forename: Daniel So
authors.initials: DS
authors.lastname: Yeung
authors.forename: Zhihang
authors.initials: Z
authors.lastname: Peng
mesh_terms: 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