Journals / Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi / 2004 / Cilt: 10 - Sayı: 4

USING NEURAL NETWORK FOR FINANCIAL APPLICATIONS ESTIMATIONS

YAPAY SİNİR AĞLARININ EKONOMİK TAHMİNLERDE KULLANILMASI

Pages
79–83
Publication Date
2004-04-01
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The calculation results which was founded by using past exchange rates data "estimations that produce by Neural Network Layers and parameters, which carry out by backpropagation algorithms for different values" was compared with the real data for measuring the productivity of the method.

📌 3 Key Takeaways

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Objective & Hypothesis:Examples of successful applications in Artificial Intelligence (AI) field; With financial applications, Control, Communication, Processing Radar signals, Pattern Recognition, general DSP application, Nonlinear Systems can be given.
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Key Findings & Data:In this application, backpropagation algorithms applied to Multi Layer Feedforward Neural Network for the future estimations of foreign currency exchange rates data.
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Conclusion & Impact:The calculation results which was founded by using past exchange rates data "estimations that produce by Neural Network Layers and parameters, which carry out by backpropagation algorithms for different values" was compared with the real data for measuring the productivity of the method.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:YSA. · Finansal Tahmin · Neural Network

Abstract

Examples of successful applications in Artificial Intelligence (AI) field; With financial applications, Control, Communication, Processing Radar signals, Pattern Recognition, general DSP application, Nonlinear Systems can be given. In the financial applications, generally back propagation (Feedforwared) algorithms of the Neural Network (NN) uses. In this application, backpropagation algorithms applied to Multi Layer Feedforward Neural Network for the future estimations of foreign currency exchange rates data. The calculation results which was founded by using past exchange rates data "estimations that produce by Neural Network Layers and parameters, which carry out by backpropagation algorithms for different values" was compared with the real data for measuring the productivity of the method.

Özet

Yapay Zekanın başarıyla uygulandığı alanlara örnek olarak; Finansal uygulamalarla birlikte, Kontrol, Haberleşme, Radar Sinyallerinin işlenmesi, örüntü tanıma, genel olarak DSP, non-lineer sistemler, vb, verilebilir. Finansal uygulamalarda, genellikle YSA Backpropagation (Feedforward–İleri Besleme) algoritması uygulanmaktadır.Bu çalışmada, Çok Katmanlı İleri Beslemeli Yapay Sinir Ağına, Eğitim Algoritması olarak Backpropagation (Geri-Yayılım) algoritmasının uygulanmasıyla farklı yabancı paralar için döviz kuru alış fiyatlarının gelecek dönemdeki değerlerinin tahmini yapılmaktadır.Geçmiş dönem kayıtları kullanılarak yapılan hesaplamalar sonucunda, Geri Yayılım algoritmasının uygulandığı Yapay Sinir Ağı bünyesindeki parametreler ve katman özelliklerinin değişik değerleri için üretilen tahmini değerler, gerçekleşen değerlerle kıyaslanmakta ve bu yolla, uygulanan tekniğin verimlililiği ölçülmektedir.

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Cite This Article

ŞEKER, M., YILDIRIM, E. S., BERKAY, A. (2004). USING NEURAL NETWORK FOR FINANCIAL APPLICATIONS ESTIMATIONS. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 10 - Sayı: 4, 79–83. https://ajindex.com/articles/43136

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