Nuclear weapon prediction based on the verhulst method of comprehensive weighting and LS-SVM equidimensional information supplement

Hongyi Duan; Jianan Zhang

doi:10.1117/12.2678905

8 May 2023 Nuclear weapon prediction based on the verhulst method of comprehensive weighting and LS-SVM equidimensional information supplement

Hongyi Duan , Jianan Zhang

Author Affiliations +

Proceedings Volume 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023); 126350S (2023) https://doi.org/10.1117/12.2678905
Event: International Conference on Algorithms, Microchips, and Network Applications 2023, 2023, Zhengzhou, China

Abstract

In this paper, to predict the nuclear weapons, we first introduce evaluation indicators that affect the possession of nuclear weapons, economic indicators, scientific and technological indicators, and establish a TOPSIS evaluation model improved by the optimal assignment method to predict countries with evaluation values less than 20, as countries that will possess nuclear weapons in the next 100 years. Then, in view of the fact that the number of nuclear weapons is calculated in years and changes over time, and considering the global consensus to limit the number of nuclear weapons from 2022 when the Treaty on the Prohibition of Nuclear Weapons and other policies come into force, it is decided to build a Verhulst prediction model with saturation based on the LS-SVM algorithm, and finally to improve the accuracy and reasonableness of the model by using the metabolic data processing method of equal-dimensional neutrosophic recurrence prediction. By predicting the number of nuclear weapons, countries can make reasonable plans for future nuclear weapons production and hope to reach a global consensus, which will help to solve the nuclear crisis.

Citation Download Citation

Hongyi Duan and Jianan Zhang "Nuclear weapon prediction based on the verhulst method of comprehensive weighting and LS-SVM equidimensional information supplement", Proc. SPIE 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023), 126350S (8 May 2023); https://doi.org/10.1117/12.2678905

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KEYWORDS

Nuclear weapons

Statistical modeling

Error analysis

Statistical analysis

Data modeling

Bombs

Linear regression

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