FTSET-a software tool for fault tolerance evaluation and improvement

• Published in: Neural computing & applications Vol. 19; no. 5; pp. 701 - 712
• Main Authors: Dias, Fernando Morgado, Borralho, Rui, Fontes, Pedro, Antunes, Ana
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.07.2010; Springer
• Subjects: Applied sciences; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer science; control theory; systems; Data Mining and Knowledge Discovery; Exact sciences and technology; Geometry; Image Processing and Computer Vision; Inference from stochastic processes; time series analysis; Learning and adaptive systems; Mathematics; Original Article; Probability and statistics; Probability and Statistics in Computer Science; Sciences and techniques of general use; Statistics; Hardware implementation; Fault tolerance; Feedforward neural networks; Graceful degradation; Fault model; Degradation; Evaluation; Neural computation; Software tool; Medical electronics; Neural network; Medical application; Sorting
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-009-0329-0

It is commonly assumed that neural networks have a built-in fault tolerance property mainly due to their parallel structure. The international community of neural networks discussed these properties until 1994 and afterward the subject has been mostly ignored. Recently, the subject was again brought to discussion due to the possibility of using neural networks in areas where fault tolerance and graceful degradation properties would be an added value, like medical applications of nano-electronics or space missions. Nevertheless, the evaluation of fault tolerance and graceful degradation characteristics remained difficult because there were no systematic methods or tools that could be easily applied to a given Artificial Neural Networks application. The discussion of models is the first step for sorting ways of developing the fault tolerance capability and for building a tool that can evaluate and improve this characteristic. The present work proposes a fault tolerance model, presents solutions for improving it and introduces the Fault Tolerance Simulation and Evaluation Tool for Artificial Neural Networks that evaluates and improves fault tolerance.