Defect Depth Estimation Using Neuro-Fuzzy System in TNDE
نویسندگان
چکیده
Abstract: Recently, supervised artificial neural networks have obtained success to reveal and provide quantitative information concerning defects in TNDE (Thermographic NonDestructive Evaluation). Supervised neural networks may converge to local minimum and their training procedure are usually long. In this study, a neuro-fuzzy approach is applied to characterize subsurface defects in TNDE. Similar to neural networks, fuzzy systems are model-free estimator systems which can learn from experience with numerical or linguistic data. In this paper, the concept of a fuzzy set and fuzzy reasoning mechanisms are first discussed. Then, a neuro-fuzzy defect depth estimator based on the Takagi-Sugeno-Kang (TSK) system modeling method is proposed. Finally, the neuro-fuzzy depth estimator is tested with both simulated and experimental TNDE data.
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