THE RELATIONSHIP BETWEEN NEURAL NETWORKS AND DEA-R (CASE STUDY: COMPANIES STOCK EXCHANGE)

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ESLAMSHOAR MARYAM; MOZAFFARI MOHAMMAD REZA

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Journal: INTERNATIONAL JOURNAL OF DATA ENVELOPMENT ANALYSIS Year:2015 | Volume:3 | Issue:2 (10) Page(s): 691-707

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Title

THE RELATIONSHIP BETWEEN NEURAL NETWORKS AND DEA-R (CASE STUDY: COMPANIES STOCK EXCHANGE)

Author(s)

ESLAMSHOAR MARYAM | MOZAFFARI MOHAMMAD REZA | Issue Writer Certificate

Keywords

DEAQ2

DEA-RQ2

EFFICIENCYQ2

NEURAL NETWORKQ2

Abstract

Evaluate the performance of companies on the Stock Exchange using non-parametric methods is very important. DEA and DEA-R with the strategies for piecewise linear frontier production function and use of available data, assess the stock company. In this study, using a NEURAL NETWORK algorithm DEA and DEA-R is suggested to classify the first companies in the stock exchange; Secondly, using the cover models in the nature of input in technology and constant returns to scale Non-decreasing scale performance on each floor with propagation NEURAL NETWORK is calculated. Thirdly, NEURAL NETWORK training and repetition, scale EFFICIENCY is determined at the end of a functional study is presented on the company's stock.

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APA: Copy

ESLAMSHOAR, MARYAM, & MOZAFFARI, MOHAMMAD REZA. (2015). THE RELATIONSHIP BETWEEN NEURAL NETWORKS AND DEA-R (CASE STUDY: COMPANIES STOCK EXCHANGE). INTERNATIONAL JOURNAL OF DATA ENVELOPMENT ANALYSIS, 3(2 (10)), 691-707. SID. https://sid.ir/paper/242706/en

Vancouver: Copy

ESLAMSHOAR MARYAM, MOZAFFARI MOHAMMAD REZA. THE RELATIONSHIP BETWEEN NEURAL NETWORKS AND DEA-R (CASE STUDY: COMPANIES STOCK EXCHANGE). INTERNATIONAL JOURNAL OF DATA ENVELOPMENT ANALYSIS[Internet]. 2015;3(2 (10)):691-707. Available from: https://sid.ir/paper/242706/en

IEEE: Copy

MARYAM ESLAMSHOAR, and MOHAMMAD REZA MOZAFFARI, “THE RELATIONSHIP BETWEEN NEURAL NETWORKS AND DEA-R (CASE STUDY: COMPANIES STOCK EXCHANGE),” INTERNATIONAL JOURNAL OF DATA ENVELOPMENT ANALYSIS, vol. 3, no. 2 (10), pp. 691–707, 2015, [Online]. Available: https://sid.ir/paper/242706/en

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