Abstract:
The paper considers the problem of studying the impact of key
determinants on the industrial enterprise business model economic efficiency
and aims to build an optimal model for predicting the industrial enterprise
business model effectiveness using neural boundaries. A system of key
determinants key factors has been developed. Significant factors were later used
to build neural networks that characterize the studied resultant trait
development vector. The procedure for constructing neural networks was
performed in the STATISTICA Neural Networks environment. As input
parameters, according to the previous analysis, 6 key factor indicators were
selected. The initial parameter is determined by economic efficiency. According
to the results of the neural network analysis, 100 neural networks were tested
and the top 5 were saved. The following types of neural network architectures,
multilayer perceptron, generalized regression network and linear network were
used. Based on the results of the neural network modeling, 5 multilayer
perceptrons of neural network architectures were proposed. According to
descriptive statistics, the best model was a multilayer perceptron, with the MLP
6-10-1 architecture, which identifies a model with 6 input variables, one output
variable and one hidden layer containing 10 hidden neurons. According to the
analysis of the sensitivity of the network to input variables, it was determined
that the network is the most sensitive to the variable the share of electricity
costs in total costs. According to the results of selected neural networks
standard prediction, the hypothesis of the best neural network was confirmed as
Absolute res., Squared res, Std. Res for the neural network MLP 6-10-1 reached the optimal value and indicate that the selected model really has small residues,
which indicates a fairly high accuracy of the forecast when using it.
