Providing a Model for Predicting the Success of Investment Projects in Free and Special Economic Zones, Using the Multi-Layer Neural Network Technique
Subject Areas : Air Pollution
morteza shokrzadeh
1
(
Department of Industrial Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran (Correspondence Officer)
)
kamaleddin rahmani
2
(
Department of Industrial Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran
)
farzin modares khiyabani
3
(
Department of Mathematics, Tabriz Branch, Islamic Azad University, Tabriz, Iran
)
majid bagherzadeh
4
(
Department of Industrial Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran
)
Keywords: investment projects, Success Prediction, Free Trade-Industrial and Special Economic Zones, Perceptron Multi-layer Neural Network,
Abstract :
To analyze the data of this research, descriptive statistics and inferential statistics were used and experts selection software, MATLAB, SPSS and PLS software were employed Using theoretical foundations and libraries, six effective factors and variables predicting the success or failure of Investment projects in the free and special economic zones of the country were identified.After describing the variables and testing the normality,using the PLS software, a confirmatory factor analysis of the variables was carried out, in which all of the factors had a good confirmatory factor analysis and all the questions were approved Then, using linear regression and ANOVA, the effect of each of the factors on the success or failure of investment projects was investigated, and the results of this test showed confirmation of the impact of each of the factors, and then the results of the hierarchical analysis indicated this was the first rank of product and service, followed by the second-rank ,that is geographical considerations, and the characteristics of the investor's psychology, the third rank, the product market characteristics, the fourth rank, the investor's ability to rank fifth, and financial considerations ,also, earned the last rank.Considering this prioritization, the neural network used in this research contained data from 6variables as an input variable, with two intermediate layers with 30 nodes in the first layer, and three nodes in the second layer, which had one outlet.The results indicated that the neural network model had the power to predict the success of the investment projects.
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