Fault localization of AI-enabled cyber–physical systems by exploiting temporal neuron activation
- 12 May 2025
- journal article
- Published by Elsevier in Journal of Systems and Software
- Vol. 229
Abstract
No abstract availableFunding Information
- JST-Mirai Program (JPMJMI20B8)
- Japan Science and Technology Corporation
- Japan Society for the Promotion of Science (JP23H03372, JP25K21179, JP23K16865)
- JST (JPMJBY24D7)
- National Natural Science Foundation of China (62302035)
This publication has 16 references indexed in Scilit:
- Machine Learning Testing: Survey, Landscapes and HorizonsIEEE Transactions on Software Engineering, 2020
- Levenberg–Marquardt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical SystemIEEE Transactions on Industrial Informatics, 2017
- A Survey on Software Fault LocalizationIEEE Transactions on Software Engineering, 2016
- Robust Statistical Methods for Empirical Software EngineeringEmpirical Software Engineering, 2016
- Simulink fault localization: an iterative statistical debugging approachSoftware Testing, Verification and Reliability, 2016
- The DStar Method for Effective Software Fault LocalizationIEEE Transactions on Reliability, 2013
- A practical evaluation of spectrum-based fault localizationJournal of Systems and Software, 2009
- Robustness of temporal logic specifications for continuous-time signalsTheoretical Computer Science, 2009
- Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous SpacesJournal of Global Optimization, 1997
- A scaled conjugate gradient algorithm for fast supervised learningNeural Networks, 1993