Artificial neural networks: a measurement of forecast learnings as potential demand
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Keywords

artificial neural networks
forecast learning
planning
production

How to Cite

Castaneda Sanchez, W. A., Polo Escobar, B. R., & Vega Huincho, F. (2023). Artificial neural networks: a measurement of forecast learnings as potential demand. Universidad Ciencia Y Tecnología, 27(118), 51-60. https://doi.org/10.47460/uct.v27i118.686

Abstract

Neural networks today have become an essential technological tool due to their contribution to developing problems related to forecasting learning in production planning to meet potential demand. The research aims to apply artificial neural networks to measure a deep understanding of forecasts. The study is quantitative with a pre-experimental design. A test with pre-test and post-test was used for the Visual Net neural network programming language for each type of programmed learning. The results based on the averages regarding the predictive conceptual understanding and the neural network method were substantial. In contrast, automatic and deep learning contributed to the development of the problem. In terms of a deep understanding of the student's behaviors is essential. The results of the application of forecasts helped the changes of the neural network in acquiring the ability to learn more complex functions and concluding that artificial neural networks in deep forecasting learning substantially improve learning in concepts, procedures, and behaviors which leads the company to minimize costs and increase profits for various ideas.

https://doi.org/10.47460/uct.v27i118.686
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References

[1] H. Beale, and Demuth, MathWorks, 2020.
[2] J. Velásquez. “Neuroscheme: un lenguaje para el modelamiento de redes neuronales artificiales”. Dyna, 72(147), pp. 75-83, 2005.
[3] E. Toro, D. Mejía, H. Salazar. “Pronóstico de ventas usando redes neuronales”. Scientia Et Technica, 10, (26), pp. 25-30, 2004. https://www.redalyc.org/articulo.oa?id=84911640006
[4] S. Hong Tang and Ch. Kit Ang, Predicting the Motion of a Robot Manipulator with Unknown Trajectories Based on an Artificial Neural Network. Mohd Khairol Anuar Bin Mohd Ariffin and Syamsiah Binti Mashohor, septiembre del 2014.
[5] J. A. Anderson, Redes Neuronales. México: Alfaomega Grupo Editor, 2007.
[6] A. Requena, R. Quintanilla, J. M. Bolarin, A. Vásquez, A. Bastidas, J. Zúñiga y L.M. Tomás. 2022. Nuevas Tecnologías y Comunicación de Atmosferas, para PYMEs. VI-3-4.
[7] G. A. Kirby, Kevin, Tutorial on Helmholtz Machine. Department of Computer Science, Northern Kentucky University, June 2006.
[8] R. F. López, and J. M. Fernández, Las redes neuronales artificiales: Netbiblo, 2008.
[9] A.A. Jiménez, C.Q. Muñoz, and F.P. Márquez, Machine learning, and neural network for maintenance management. Paper presented at the International Conference on Management Science and Engineering Management, 2017.
[10] R. López, Las Redes neuronales artificiales: fundamentos teóricos y aplicaciones prácticas. Oleiros, La Coruña Netball, 2019.
[11] Y. LeCun, Y. “Bengio, and G. Hinton, Deep learning.” Nature, 521(7553), 436-444. 2015. https://doi.org/10.1038/nature14539
[12] F. Marton, and R. Säljö, “On qualitative differences in learning. II. Outcome as a function of the learner's conception of the task”. British Journal of Educational Psychology, 46, 128-148. 1976.
[13] J. Biggs, and K. F. Collins, Evaluating the quality of learning: The Solo Taxonomy. Nueva York: Academic Press, 1982.
[14] I. Goodfellow, Y. Bengio, and A. Courville, (2016). Deep Learning Adaptive Computation and Machine Learning Series).Estados Unidos: MIT Press,2016. Obtenido de https://www.deeplearningbook.org/contents/convnets.html.
[15] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning.” Nature, 521(7553), 436-444, 2015.
https://doi.org/10.1038/nature14539.
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