A neural network sensitivity analysis in the presence of random fluctuations
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- 作者：K.G. Kapanova ; kapanova@parallel.bas.bg ; kkapanova@gmail.comAuthor Vitae ; I. DimovAuthor Vitae ; J.M. SellierAuthor Vitae
- 关键词：Neural networks ; Feedforward networks ; Random search ; Simulated annealing ; Sensitivity analysis ; Function approximation
- 刊名：Neurocomputing
- 出版年：2017
- 出版时间：8 February 2017
- 年：2017
- 卷：224
- 期：Complete
- 页码：177-183
- 全文大小：671 K
- 卷排序：224
文摘
We introduce a sensitivity analysis (using three indicators) of solutions provided by a neural network in the presence of noise in the weights. We propose a relatively uncommon (in the neural network community) mathematical tool that allows to draw quantitative relevant conclusions for the performance of the system in the presence of noise in the weights. we show that a certain amount of perturbation in the set of weights can be, under particular conditions, an advantage. We provide two numerical experiments to showcase the method for calculating the noise in the network and have applied three indicators in the study-Euclidean distance (L2), cosine similarity (Lcos), and L_inf.