This study delves into a comprehensive comparison between Fuzzy Logic (FL) and Artificial Neural Networks (ANN) in the context of the Inverse Depletion problem. Both methodologies, recognized for their distinct capabilities in handling complex problems, are assessed for their efficacy, accuracy, and computational efficiency. Initial observations highlighted the inherent flexibility of FL in managing uncertainty and the adaptive nature of ANN in recognizing patterns from intricate datasets. A series of benchmark scenarios were established to gauge the performance of each model. Results indicate that while FL offers more interpretable solutions, ANNs often outpace in terms of prediction accuracy. However, the choice between the two largely hinges on the specific requirements of the problem at hand, including the available data quality and the desired output precision. This research underscores the importance of understanding the nuances of each method and provides insights to practitioners on selecting the optimal approach for tackling the Inverse Depletion problem in the field of nuclear forensics.
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