Academic Journals Database
Disseminating quality controlled scientific knowledge

New Metrics for Sensitivity Analysis of FFANN

ADD TO MY LIST
 
Author(s): Amit Prakash Singh | Pravin Chandra | Chandra Shekhar Rai

Journal: International Journal of Engineering Science and Technology
ISSN 0975-5462

Volume: 2;
Issue: 4;
Start page: 322;
Date: 2010;
VIEW PDF   PDF DOWNLOAD PDF   Download PDF Original page

Keywords: Artificial Neural Network | Weight Fault | Sensitivity Metrics | Fault-Tolerance

ABSTRACT
An important issue in the design of a neural network is the sensitivity of its output to input and weight faults. In this paper, new fault metrics for sensitivity analysis is derived. The experimental derivation of the derived fault metrics is demonstrate that upper bound envelop exist with empirical error of the neural network. Correlation coefficient between MEANWSZ and derived sensitivity analysis for each problem concludes that derived fault metric gives the better results.
Affiliate Program      Why do you need a reservation system?