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A distributed fault identification protocol for wireless and mobile ad hoc networks
(Academic Press, 2008)
This paper considers the problem of self-diagnosis of wireless and mobile ad hoc networks (MANETs) using the comparison approach. In this approach, a network (MANET) consists of a collection of n independent heterogeneous ...
A parallel genetic algorithm for identifying faults in large diagnosable systems
(Taylor & Francis GroupAbingdon, UK, 2005-06)
This paper deals with the problem of fault identification in large diagnosable systems under the PMC model. Recently, genetic algorithms have been successfully used to solve this system-level fault diagnosis problem; ...
Fault diagnosis using partial syndromes: a modified Hopfield neural network approach
(Taylor & Francis, 2014-03)
This paper presents a modified Hopfield neural network (HNN) for solving the system-level fault diagnosis problem which aims at identifying the set of faulty nodes. This problem has been extensively studied in the last ...
A Perceptron Neural Network for Asymmetric Comparison-Based System-Level Fault Diagnosis
(IEEE, 2009-03)
The system-level fault diagnosis problem aims at answering the very simple question "Who's faulty and who's fault-free?", in systems known to be diagnosable. In this paper, we answer such a question using neural networks. ...
Solving the pmc-based system-level fault diagnosis problem using hopfield neural networks
(IEEE, 2011-03)
This paper presents a modified Hop field neural network (HopfieldNN) for solving the PMC-based system-level fault diagnosis problem of multiprocessor systems which aims at identifying the set of faulty processors. The ...
A modified Hopfield neural network for diagnosing comparison-based multiprocessor systems using partial syndromes
(IEEE, 2011-12)
A modified Hop field neural network is introduced to solve the comparison-based system-level fault diagnosis problem when only partial syndromes are available. We use the generalized comparison model, where a set of tasks ...
Using support vector machines to solve the comparison-based system-level fault diagnosis problem
(Taylor & Francis, 2016-03)
This paper introduces a new system-level fault diagnosis approach using support vector machines (SVMs). The objective of the fault diagnosis problem is to identify the set of permanent faulty nodes when at most nodes can ...
A novel generalized-comparison-based self-diagnosis algorithm for multiprocessor and multicomputer systems using a multilayered neural network
(IEEE, 2010-12)
We consider the system-level self-diagnosis of multiprocessor and multicomputer systems under the generalized comparison model (GCM). In this diagnosis model, a set of tasks is assigned to pairs of nodes and their outcomes ...
Performance analysis of an evolutionary algorithm for fault detection in t-diagnosable multi-processor systems
(IEEE, 2017-10)
In this paper, we present a performance analysis of an evolutionary approach for fault identification in t-diagnosable systems, i.e. systems in which the set of t permanently faulty units could be unambiguously identified. ...
Using linear support vector machines to solve the asymmetric comparison-based fault diagnosis problem
(IEEE, 2012-08)
This paper presents a new diagnosis approach, using linear support vector machines (SVMs). The objective is to identify the set of permanent faulty nodes when at most t nodes can fail simultaneously. We consider the ...