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Reliability analysis of man-machine systems using fuzzy cognitive mapping with genetic tuning.
Alexander Rotshtein1,2, Denis Katelnikov3, Ludmila Pustylnik4
1Department of Industrial Engineering, Jerusalem College of Technology, Jerusalem, Israel.
This study introduces a novel method for assessing man-machine system (MMS) reliability using fuzzy cognitive maps (FCMs). By adjusting FCMs with genetic algorithms, it significantly improves reliability predictions in complex systems.
Area of Science:
- Systems Engineering
- Reliability Engineering
- Computational Intelligence
Background:
- Man-machine systems (MMS) reliability is crucial in complex environments.
- Traditional methods struggle with vaguely defined structures and expert-driven factors.
- Fuzzy Cognitive Maps (FCMs) offer a framework for modeling such systems.
Purpose of the Study:
- To develop and validate a novel method for analyzing MMS reliability.
- To rank influencing factors affecting system reliability.
- To enhance the accuracy of reliability predictions in complex, expert-dependent systems.
Main Methods:
- Utilized Fuzzy Cognitive Maps (FCMs) for system modeling.
- Employed the relationship of variable increments to approximate factor-reliability dependence.
- Applied a genetic algorithm to adjust FCM edge weights based on observational data.
- Validated the method using a driver-car-road system example.
Main Results:
- The proposed method effectively ranks influencing factors on system reliability.
- Adjusting FCMs with genetic algorithms significantly reduced prediction errors.
- Discrepancy between simulated and observed reliability decreased by nearly half.
- Demonstrated applicability to complex systems with expert-defined interdependencies.
Conclusions:
- The FCM-based method with genetic algorithm optimization provides a robust approach to MMS reliability analysis.
- This technique enhances the accuracy of reliability forecasting in systems with complex, interrelated factors.
- The method is particularly valuable for systems where precise structural definition is challenging and expert knowledge is paramount.
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