Mendeley
TY  _  JOUR
ID  -  1397061915526164283
TI  -  Prediction of Growth of Small and Medium Enterprises with the Combination of Artificial Neural Networks and Meta-Heuristic Algorithm
JO  -  Modiriat-e- farda
JA  -  ES
LA  -  fa
SN  -  2228-6047
AU  -  ابراهیم خانی حامد
AU  -  کاظمی مصطفی
AU  -  Pooya Alireza
AU  -   Fakoor Saghih Amir Mohammad
AD  -  علوم اداری و اقتصاد
AD  -  
AD  -  دانشگاه فردوسی مشهد
AD  -  دانشگاه فردوسی مشهد
Y1  -  2019
PY  -  2019
VL  -  60
IS  -  
SP  -  205
EP  -  220
KW  -  Firm Growth
 Artificial Neural Network
 Meta-heuristic algorithms
DO  -  
N2  -  The growth of a company is considered to be an important economic goal. Given that many small and medium enterprises do not grow into growth and fail in the early years of their operations, a predictive system of corporate growth can be offset by the huge costs Starting businesses, entrepreneurs and companies to pay. Accordingly, the purpose of this study was to predict the growth of small and medium enterprises with the combination of neural network and meta-heuristic algorithms. The purpose of this research was applied and based on the method of doing descriptive-modeling work. Statistical population of this research was all small and medium enterprises of Zanjan province. Statistical sample size According to the growth of companies, 158 companies has been designated. In order to collect data in this study, interviews, questionnaires and documents of companies have been used. Validity and reliability of the questionnaire were verified and and using Cronbach's alpha coefficient. In order to analyze the research data using confirmatory factor analysis methods, the neural network of multilayer perceptron, neural network combined with genetic algorithm and neural network combined with particle swarm algorithm have been used. The results show that all three methods are able to predict the growth of the company. Among these three methods, the best predictive method for growth of the company is the neural network combined with the particle swarm algorithm with the least error rate compared to the other two methods. 
UR  - rimag.ir/en/Article/25788
L1  - rimag.ir/en/Article/Download/25788
ER  -  
