International Journal of Man-Machine Studies
Using fuzzy cognitive maps as a system model for failure modes and effects analysis
Information Sciences: an International Journal
Fuzzy engineering
Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links
International Journal of Human-Computer Studies
Fuzzy Cognitive Maps for stereovision matching
Pattern Recognition
Hybrid neural network models for hydrologic time series forecasting
Applied Soft Computing
Brain tumor characterization using the soft computing technique of fuzzy cognitive maps
Applied Soft Computing
Fuzzy cognitive map architectures for medical decision support systems
Applied Soft Computing
Intrusion detection using fuzzy association rules
Applied Soft Computing
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
A new hybrid method using evolutionary algorithms to train Fuzzy Cognitive Maps
Applied Soft Computing
Advanced soft computing diagnosis method for tumour grading
Artificial Intelligence in Medicine
Numerical and Linguistic Prediction of Time Series With the Use of Fuzzy Cognitive Maps
IEEE Transactions on Fuzzy Systems
Damage detection based on improved particle swarm optimization using vibration data
Applied Soft Computing
Knowledge-Based Systems
A flexible nonlinear approach to represent cause-effect relationships in FCMs
Applied Soft Computing
Learning method inspired on swarm intelligence for fuzzy cognitive maps: travel behaviour modelling
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Advances in Fuzzy Systems - Special issue on Real-Life Applications of Fuzzy Logic
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A new algorithmic approach for structural damage detection based on the fuzzy cognitive map (FCM) is developed in this paper. Structural damage is modeled using the continuum mechanics approach as a loss of stiffness at the damaged location. A finite element model of a cantilever beam is used to calculate the change in the first six beam frequencies because of structural damage. The measurement deviations due to damage are fuzzified and then mapped to a set of faults using FCM. The input concepts for the FCM are the frequency deviations and the output of the FCM is at five possible damage locations along the beam. The FCM works quite well for structural damage detection for ideal and noisy data. Further improvement in performance is obtained when an unsupervised neural network approach based on Hebbian learning is used to evolve the FCM. Numerical results clearly show that the use of FCM and Hebbian learning results in accurate damage detection and represents a powerful tool for structural health monitoring.