Abstract:
This study aimed to develop a recognition model in
order to classify success of
agricultural enterprises. To this end, the study in
vestigated the relationship between
capitals owned by the enterprise and the success le
vel by using "Neural Network" model.
This study was conducted during 2013-2014 in Zanjan
County, Islamic Republic of Iran.
Data was obtained through a structured questionnair
e and holding interviews with 92
enterprise owners, out of 125, involved in producin
g agrifood. According to the results of
data analysis, Multilayer Perceptron Neural Network
with Backpropagation algorithm
was the appropriate algorithm to cope with the whol
e scope of the study. Empirical
analysis by SPSS indicated that the Multilayer Perc
eptron consisting of one hidden layer
with 6 nodes was an appropriate architecture. Class
ification Accuracy Rate (CAR) and
"Receiver Operating Characteristic (ROC)" curve wer
e used to assess the model. Based
on CAR of holdout data, the model was able to class
ify 86.4% of the samples correctly.
Also, the study intended to reveal the relative imp
ortance of explanatory factors on
enterprise success. Results indicated that human an
d social capitals were the more
influential factors, followed by economic and envir
onmental capitals. Therefore, to
promote agricultural enterprises, policy makers and
managers need to improve software
and hardware assets, simultaneously.
Keywords:
Classification, Neural Network, Perceptron, Pattern
.
