THE ROLE OF ROSENBLATT'S PERCEPTRON IN DECISION-MAKING FOR ANALYZING ECONOMIC PROCESSES
Keywords:
Rosenblatt’s Perceptron, econometrics, personal finance, industrial automation, adaptive decision-making, financial management, budgeting, savings and spending, machine learning, risk management, predictive analysis, ethical considerations, decision-making framework.Abstract
This article examines the transformative role of Rosenblatt’s Perceptron in econometrics and financial decision-making, emphasizing its influence on personal finance, industrial automation, and human-computer interaction. Rosenblatt’s Perceptron, inspired by neural structures in the human brain, offers a dynamic learning mechanism capable of adapting to data patterns over time. In personal finance, it optimizes budgeting, savings, and expenditure management through its adaptive weight adjustments. The model’s application in industrial automation enhances decision-making in resource allocation and process optimization, significantly improving efficiency and accuracy. Additionally, the article highlights the perceptron’s role in econometrics to analyze financial patterns, detect fraud, and manage risks more effectively. Ethical considerations, such as addressing biases, ensuring data privacy, and maintaining transparency in algorithmic processes, are also discussed, underscoring the importance of responsible technological deployment. Ultimately, the article concludes that despite challenges, the integration of Rosenblatt’s Perceptron into econometric models and financial systems offers indispensable benefits for advancing modern decision-making frameworks.
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References
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115-133.
Rumelhart, D. E., Hinton, G. E Schumaker, R. P., & Chen, H. (2009). Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems (TOIS), 27(2), 1-19.., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533-536.
Schumaker, R. P., & Chen, H. (2009). Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems (TOIS), 27(2), 1-19.
Rakhimov, A. N., Makhmatkulov, G. K., & Rakhimov, A. M. (2021). Construction of econometric models of development of services for the population in the region and forecasting them. The American Journal of Applied sciences, 3(02), 21-48.
Mukhitdinov, K. S., & Rakhimov, A. M. Рroviding accommodation and food services to the population of the region. International Journal of Trend in Scientific Research and Development (IJTSRD), eISSN, 2456-6470.
Жураев, Ф. (2021). Перспективные проблемы развития производство сельскохозяйственной продукции и их эконометрическое моделирование. Экономика И Образование, (4), 377-385.
Raximov, A. N. (2023). Dehqon xo ‘jaliklari faoliyatining istiqbolli rivojlantirishga tasir etuvchi omillar. Экономика и социум, (3-2 (106)), 255-262.
Maxmatqulov, G. O. X. (2023). Savdo xizmatlari tarmog ‘ini rivojlantirish masalalariga tizimli yondoshuv. Educational Research in Universal Sciences, 2(10), 175-182.
Xoliqulovich, J. A., Islomnur, I., & Normurodovich, M. S. (2023). Advanced control-goals and objectives. technologies of built-in advanced control in deltav APCS. Galaxy International Interdisciplinary Research Journal, 11(2), 357-362.
Jo‘rayev, F. D. S., & Normurod o‘g‘li, M. S. (2024). Aholi turmush darajasini oshirishning ekonometrik modellashtirishida mcculloch-pitts neyron modeli qo ‘llanilishi. prospects and main trends in modern science, 2(16), 40-45.
