Determining the Most Important Variables for Women’s Entrepreneurship Crisis Management by Neuro Fuzzy System

被引:0
作者
Lakovic V. [1 ]
机构
[1] Fakulteta društvenih znanosti dr. Milenka Brkića, Bijakovići
关键词
ANFIS; Business; Female entrepreneurial activity; Forecasting;
D O I
10.1007/s40745-020-00252-6
中图分类号
学科分类号
摘要
Without the active participation of women in all facets of life economic development cannot be accomplished. There is consensus among scholars that women may play a key role in the phenomenon of entrepreneurship. The proportion of the contribution of women to economic and social development depends on the institutions ' promotion of gender equality and gender blind support. While women make up about fifty per cent of the world’s population, they have less opportunity to control their lives and make decisions than men. Women entrepreneurs have been named to bring prosperity and education to the developing countries as the new growth drivers and the rising stars of economies. The main objective of the analysis was to examine the impact of internal market dynamics, internal market transparency, physical and service infrastructure and cultural and social norms on the forecasting of total and female entrepreneurial activities. Since this is the highly nonlinear job the soft computing approach has been applied in this analysis. The Process for ANFIS (adaptive neuro fuzzy inference system) was applied to determine the most important variables for both total and female entrepreneurial activity. To order to better understand this trend, rapid growth and involvement of women to entrepreneurship and the growing research body creates a need for both general and unique theoretical perspectives and approaches. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
引用
收藏
页码:385 / 392
页数:7
相关论文
共 28 条
[21]  
Petkovic D., Cojbasic Z., Adaptive neuro-fuzzy estimation of automatic nervous system parameters effect on heart rate variability, Neural Comput Appl, 21, 8, pp. 2065-2070, (2012)
[22]  
Kurnaz S., Cetin O., Kaynak O., Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles, Exp Syst Appl, 37, pp. 1229-1234, (2010)
[23]  
Petkovic D., Issa M., Pavlovic N.D., Zentner L., Cojbasic Z., Adaptive neuro fuzzy controller for adaptive compliant robotic gripper, Exp Syst Appl ISSN, 957-4174, 39, pp. 13295-13304, (2012)
[24]  
Tian L., Collins C., Adaptive neuro-fuzzy control of a flexible manipulator, Mechatronics, 15, pp. 1305-1320, (2005)
[25]  
Ekici B.B., Aksoy U.T., Prediction of building energy needs in early stage of design by using ANFIS, Exp Syst Appl, 38, (2011)
[26]  
Khajeh A., Modarress H., Rezaee B., Application of adaptive neuro-fuzzy inference system for solubility prediction of carbon dioxide in polymers, Expert Syst Appl, 36, (2009)
[27]  
Inal M., Determination of dielectric properties of insulator materials by means of ANFIS: a comparative study, Exp Syst Appl, 195, (2008)
[28]  
Lo S.P., Lin Y.Y., The prediction of wafer surface non-uniformity using FEM and ANFIS in the chemical mechanical polishing process, J Mater Process Technol, 168, (2005)