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Comparing the neural network with path analysis in fitting regression models | ||
| Journal of Hyperstructures | ||
| دوره 9، شماره 1، شهریور 2020، صفحه 1-10 اصل مقاله (376.87 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22098/jhs.2020.2623 | ||
| نویسندگان | ||
| Fereshteh Aard* ؛ Ayyub Sheikhi | ||
| Department of Statistics, Shahid Bahonar University Kerman, Iran | ||
| چکیده | ||
| The purpose of this study was to compare a neural network with the path analysis in fitting regression models. The conceptual model of path analysis according to the studied data includes a dependent variable, two independent variables and one mediating variable. The neural network conceptual model is considered with three layers (input, hidden, output) and the hidden layers have two nodes. The study asked 474 people about their education, beginning salary, previous experience and their current salaries. The data divided into the train and test groups at the rate of %60 and %40. The criterion for comparing the two methods is RMSE. The results of the analysis showed that both models are over fitted and the RMSE train and test of neural network are less different from the path analysis. Therefore, in this dataset, it can be said that the neural network performs better than the path analysis. | ||
| کلیدواژهها | ||
| Neural network؛ Path analysis؛ Regression؛ RMSE | ||
| مراجع | ||
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[1] A. Sheikhi, Analysis and statistical modeling, Shahih Bahonar university press, (2019), (in persian). [2] M. Hosseini, Introduction to neural network, (2007). [3] M. B. Menhaj, Computational intelligence, Amir Kabir university, (2018). [4] H. Breck, Neural network computers nding practical application at lockheed. J. Algebra, 91 (1990). [5] J. Hop eld, Neural networks and physical systems with emergent collective computational abilities, Proceeding of the National Academy of Sciences of the USA, 2554-2558, (1982). [6] R. Brian, Pattern recognition and neural network, Cambridge university press, 88, (1996). [7] R. Frank, The Perceptron-a perceiving and recognizing automaton, (1975). [8] T. A. A, Decision support system and intelligent system, Prentice-Hall, 334, (1998). | ||
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