Research on the synthesis of hydrotalcite and RBF neural network prediction for simulating near-infrared water peaks in jungle camouflage

QIU Chengcong¹, FAN Xinyu¹, PAN Guoxiang¹, XU bo¹, XU minhong¹, LI jinhua²

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Chinese Journal of Light Scattering ›› 2024, Vol. 36 ›› Issue (4) : 392-398. DOI: 10.13883/j.issn1004-5929.202404004

Research on the synthesis of hydrotalcite and RBF neural network prediction for simulating near-infrared water peaks in jungle camouflage

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Abstract

A series of hydrotalcite materials are prepared using hydrothermal synthesis meth- od,and their spectral near-infrared water absorption peaks are tested using a visible near-in- frared spectrometer to simulate the near-infrared water peaks in jungle camouflage coating materials.The influence of experimental conditions on the size of water peaks is explored.A PSO-RBF predictive model is constructed using partial serpentine spectroscopic data and veri- fies the reliability of the model.The validation results indicate that the predictive accuracy of model exceeds 90%,demonstrating excellent practical capability.Employing the predictive model to forecast the development of serpentine facilitates the determination of optimal prep- aration conditions,thereby reducing experimental time and further enhancing the water ab- sorption peak of traditionally synthesized serpentine products for camouflage applications.

Key words

Jungle / camouflage / Layered / Double / Hydroxide / Visible-near / infrared / spectrosco- py / Radial / basis / function / neural / network / Prediction

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QIU Chengcong¹, FAN Xinyu¹, PAN Guoxiang¹, XU bo¹, XU minhong¹, LI jinhua². Research on the synthesis of hydrotalcite and RBF neural network prediction for simulating near-infrared water peaks in jungle camouflage. Chinese Journal of Light Scattering. 2024, 36(4): 392-398 https://doi.org/10.13883/j.issn1004-5929.202404004

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