2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 9 , Issue 1 , PP: 17-25, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Jamshid S. Tukhtabaev 1 * , Umid A. Otajanov 2 , Shakhnoza T. Nurullaeva 3 , Saodat A. Saydullaeva 4 , Gulshan M. Abdulxayeva 5
Doi: https://doi.org/10.54216/AJBOR.090102
In this article, the artificial neural network mathematical model is used for Regional Investment Distribution Processes Analysis in the regions of the Republic of Uzbekistan; forecasts are made using this model, and the results are compared with the results determined using the trend and panel methods, and the preferred method is defined. Multi-layer perceptron, radial-basis grid, generalized-regression grid, and recurrent grid can be used to solve the forecasting problem. In the study, a program for intellectual analysis and forecasting of socio-economic development indicators of the regions of the Republic of Uzbekistan using the generalized regression network was developed. This program makes it possible to extract the most necessary factors from other methods even in the presence of multi-factor indicators and determine the future forecast result under their influence. According to the results of the forecast determined using the intellectual mathematical model developed because of the research, by 2025, the volume of the gross regional product of Andijan region is expected to be equal to 95607.34 billion soums, and in Samarkand region, it is expected to be equal to 80419.73 billion soums, in Tashkent city it is expected to be 259301.8 billion soums. According to the error levels of the results determined by the intellectual mathematical method, it represents an average error of 1.13% compared to the forecast period. If we determine the result of the trend by the level of error, it is equal to an average of 4.96% error compared to the forecast period, and these results prove the superiority of the intellectual mathematical method developed in the research.
Intelligent models , gross regional product , investment , model , neural network , layer , intellectual mathematical model , investment potential forecast.
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