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Development of an Intelligent Software Based on Adaptive Neural-Fuzzy Inference Systems for Predicting Muzzle Vibration of a Gun Barrel

  • Research Article-Mechanical Engineering
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Abstract

The idea of using long barrels in weapon systems provides a significant advantage in terms of superiority against targets moving at high speed. However, the use of long barrels in weapon systems has a disadvantage in terms of target precision. In particular, the ability to use multiple and different ammunition in a weapon system causes the barrel dynamics to be different for each ammunition. This situation makes it even more difficult to control muzzle vibrations. In this study, the adaptive neural-fuzzy model was designed to estimate the vibrations at the tip of an antiaircraft barrel, considering different ammunition. The sample sets used for training and testing the adaptive neural-fuzzy model were obtained using the finite element method, taking into account the dynamic interaction between the accelerated projectile and the barrel. The performance of the adaptive neural-fuzzy model created has been tested with the cases created by considering the different membership function numbers. Apart from this, the performance of the adaptive neural-fuzzy model is compared with artificial neural networks, and the results are given in a table. With the adaptive neural-fuzzy model, the mean square error value obtained by using test patterns was 0.002, the correlation coefficient R2 value was 0.9998, and the mean error percentage value was 1.6. As a result, it has been proven that adaptive neural-fuzzy outperforms ANN in predicting barrel tip vibrations.

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  1. Department of Mechatronics Engineering, Sakarya University of Applied Sciences, 54187, Serdivan, Sakarya, Turkey

    Mehmet Akif Koç

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  1. Mehmet Akif Koç
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Koç, M.A. Development of an Intelligent Software Based on Adaptive Neural-Fuzzy Inference Systems for Predicting Muzzle Vibration of a Gun Barrel. Arab J Sci Eng 47, 8829–8846 (2022). https://doi.org/10.1007/s13369-021-06425-6

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  • DOI: https://doi.org/10.1007/s13369-021-06425-6

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