Fang, Z.;                     Zhao, H.;                     Feng, Y.;                     Wu, Y.;                     Sun, Y.;                     Yang, Q.;                     Zheng, G.    
        Field Strength Prediction in High-Speed Train Carriages Using a Multi-Neural Network Ensemble Model with Optimized Output Weights. Appl. Sci. 2025, 15, 2709.
    https://doi.org/10.3390/app15052709
    AMA Style
    
                                Fang Z,                                 Zhao H,                                 Feng Y,                                 Wu Y,                                 Sun Y,                                 Yang Q,                                 Zheng G.        
                Field Strength Prediction in High-Speed Train Carriages Using a Multi-Neural Network Ensemble Model with Optimized Output Weights. Applied Sciences. 2025; 15(5):2709.
        https://doi.org/10.3390/app15052709
    
    Chicago/Turabian Style
    
                                Fang, Zhou,                                 Hengkai Zhao,                                 Yichen Feng,                                 Yating Wu,                                 Yanqiong Sun,                                 Qi Yang,                                 and Guoxin Zheng.        
                2025. "Field Strength Prediction in High-Speed Train Carriages Using a Multi-Neural Network Ensemble Model with Optimized Output Weights" Applied Sciences 15, no. 5: 2709.
        https://doi.org/10.3390/app15052709
    
    APA Style
    
                                Fang, Z.,                                 Zhao, H.,                                 Feng, Y.,                                 Wu, Y.,                                 Sun, Y.,                                 Yang, Q.,                                 & Zheng, G.        
        
        (2025). Field Strength Prediction in High-Speed Train Carriages Using a Multi-Neural Network Ensemble Model with Optimized Output Weights. Applied Sciences, 15(5), 2709.
        https://doi.org/10.3390/app15052709