A comparative study: fuzzy logic and ANN in addressing inverse depletion

Bassam A. Khuwaileh; Mohammad Alshabi; Polina Matesha

doi:10.1117/12.3013867

7 June 2024 A comparative study: fuzzy logic and ANN in addressing inverse depletion

Bassam A. Khuwaileh, Mohammad Alshabi, Polina Matesha

Proceedings Volume 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI; 130511Q (2024) https://doi.org/10.1117/12.3013867
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bassam A. Khuwaileh, Mohammad Alshabi, and Polina Matesha "A comparative study: fuzzy logic and ANN in addressing inverse depletion", Proc. SPIE 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI, 130511Q (7 June 2024); https://doi.org/10.1117/12.3013867

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