Evaluation of Fuzzy Labor by Fuzzy neural Network
Subject Areas : Labor and Demographic Economics
Keywords: Labor Market, Fuzzy Neural Network, Fuzzy number, Fuzzy Regression,
Abstract :
In this paper, a new hybrid method based on fuzzy neural on fuzzy neural network for estimating the fuzzy coefficients (parameters) of fuzzy supply and demanding the labor function with fuzzy inputs, is presented. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples.
- منابع
- Alefeld,G. , Herzberger,J.,1983. Introduction to Interval Computations, Academic Press, New York.
- Change,P.,T.,1997. Fuzzy seasonality forecasting, Fuzzy Sets Syst. 90,1-10
- Chen,T., Wang,M.,J.,J.,1999. Forecasting methods using fuzzy concepts, Fuzzy Sets Syst. 105,339-352
- Conway, K., S., Kniesner, T., J., 1992. How fragile are male labor supply function estimates, Empirical Economics 17, 179-182
- Cowell, A., F., 2004. Microeconomics principles and analysis, Sticerd and department of economics , London school of economics.
- Feuring, TH., Lippe,W., M., 1995. Fuzzy neural networks are universal approximators, IFSA World Congress, Sao Paulo, Brasil,2,659-662.
- Jorgenson, W., D., Goettle, R., J., Ho, S., M., Slesnick, T., D., Wilcoxen, J., P., 2008. U.S. Labor supply and demand in the long run. Journal of policy modeling 30, 603 – 618.
- Heckman, J., J., Macurdy, T., E., 1980. A life-cycle model of female labor supply, review of economic studies, 47, 47 – 74.
- Henderson, J., M., Quandt , R., E., 1985. Microeconomic theory a math-ematical approach, Third edition, MCGRAW-HILL.
- Ishibuchi, H., Nii,M.,2001. Numerical analysis of the learning of fuzzified neural networks from fuzzy if-then rules, Fuzzy Sets and Systems 120,281-307.
- Kaleva, O.,1987. Fuzzy differential equations ,Fuzzy Sets and Systems 24,301-317
- Kao,C., Chyu, C., L.,2003. Least-squares estimates in fuzzy regression analysis. European Journal of Operational Research 148,426-435.
- Li,H., X., Li,L., X., Wang,J., Y., 2003. Interpolation function of feed-forward neural networks, Computers and mathematics with applications 46,1861-1874.
- Ma,M., FriedmanM., Kandel,A., 1999. A new Fuzzy arithmetic ,Fuzzy Sets and Systems 108,83-90.
- Moffitt, R., 1993. Identification and estimation of dynamic with a time series of repeated.Journal of economics 59,99-123.
- Mosleh,M., Otadi,M., Abbasbandy,S.,2010. Evaluation of fuzzy regression models by fuzzy neural network, JORNAL OF computational and Applied Mathematics, 234, 825-834.
- Pesaran, M.H., Smith, R., 1995. Estimation long-run relationships from dynamic heterogynous panels, Journal of econometries 68,79-113.
- Rumelhart,D., E., Mcclelland,J.,L.,PDP Research Griup, 1986. Parallel Distributed Processing ,Vpl. 1, MIT Press, Cambridge, MA.
- Tanaka, H., Hayashi,I., Watada,J., 1989. Possibilistic linear regression analysis for fuzzy data, European Journal of Operational Research 40,389-396.
- Tseng,f.,M., Tzeng,G.,H.,2002. A fuzzy seasonal ARIMA model for forecasting , Fuzzy Sets and Syst. 126,367-376.
- Verbon, H., A., A., 1980. Testfor heteroscedasticity in a model of seem-ingly unrelated regression equation with variance components ( SUREVC), Economic letters 5,149-153.
- Zadeh, L., A., 1975. The concept of a linguistic variable and application to approximate reasoning, Inform Sci. 8.199-249.