RESEARCH OF THE FACTORS DETERMINING THE PATTERNS OF ENTREPRENEURSHIP DEVELOPMENT

2020 ◽  
Vol 1 (9) ◽  
pp. 105-113
Author(s):  
E. V. BONDARENKO ◽  
Keyword(s):  
Neural Network ◽  
Gross Domestic Product ◽  
Network Modeling ◽  
Entrepreneurial Activity ◽  
Neural Network Modeling ◽  
Entrepreneurship Development ◽  
Fixed Assets ◽  
Main Factors ◽  
Investment In Fixed Assets

The paper investigates the patterns of entrepreneurship development by analyzing such indicators as: gross domestic product and turnover of small and medium-sized businesses. The following factors were selected and analyzed as the main factors that determine the tendencies of changes in entrepreneurial activity: investments in fixed assets at actual prices, as well as the purchase of goods and payment for services – consumption. The functions of indicators of consumption, investment in fixed assets, GDP and SME turnover have been formulated. A neural network modeling of the above functions and patterns of entrepreneurship development has been performed. The quality of the constructed models and the influence of factors determining the patterns of changes in entrepreneurial activity are assessed.

PREDICTING SOFTWARE QUALITY, DURING TESTING, USING NEURAL NETWORK MODELS: A COMPARATIVE STUDY

1994 ◽  
Vol 01 (03) ◽  
pp. 303-319 ◽  
Author(s):  
TAGHI M. KHOSHGOFTAAR ◽  
ROBERT M. SZABO
Keyword(s):  
Neural Network ◽  
Software Quality ◽  
Principal Components ◽  
Network Modeling ◽  
Network Models ◽  
Neural Nets ◽  
Neural Network Modeling ◽  
Neural Network Models ◽  
Predictive Quality

The application of statistical modeling techniques has been an intensely pursued area of research in the field of software engineering. The goal has been to model software quality and use that information to better understand the software development process. Neural network modeling methods have recently been applied to this field. The results reported indicate that neural network models have better predictive quality than some statistical models when predicting reliability and the number of faults. In this paper, we will investigate the application of principal components analysis to neural network modeling as a way of improving the predictive quality of neural network quality models. Using data we collected from a large commercial software system, we developed a multiple regression model using the principal components. Then, we trained two neural nets, one with raw data, and one with principal components. Then, we compare the predictive quality of the three competing models for a variety of quality measures.

Water quality parameter forecast based on grey neural network modeling

2009 ◽  
Vol 29 (6) ◽  
pp. 1529-1531 ◽  
Author(s):  
Wei-ren SHI ◽  
Yan-xia WANG ◽  
Yun-jian TANG ◽  
Min FAN
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Network Modeling ◽  
Quality Parameter ◽  
Water Quality Parameter ◽  
Neural Network Modeling

ε-Insensitive Criterion and Structure Risk Based Radius-basis-function Neural-network Modeling

2012 ◽  
Vol 34 (6) ◽  
pp. 1414-1419
Author(s):  
Qing-bing Sang ◽  
Zhao-hong Deng ◽  
Shi-tong Wang ◽  
Xiao-jun Wu
Keyword(s):  
Neural Network ◽  
Basis Function ◽  
Network Modeling ◽  
Neural Network Modeling
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