Fuzzy Inference Modeling Methodology for the Simulation of Population Growth
Hassan Diab and Jean Saade
Department of Electrical and Computer Engineering, American University of Beirut, Lebanon
Abstract: This paper presents the use of fuzzy inference to provide a viable modeling and simulation methodology for the estimation of population growth in any country or region. The study is motivated by the classical complex and time-consuming growth modeling and prediction methods. The related design issues are presented and the fuzzy inference model for population growth is derived. The human social and economic factors which affect the growth and which underly the parameters used in the classical population projection methods are fuzzified. They are then used as inputs to a fuzzy population growth model based on fuzzy inferences so as the population growth rate is evaluated. The fuzzy population model is simulated using an existing CAD tool for fuzzy inference which has been developed and described elsewhere by the authors. The results obtained using different existing defuzzification strategies and a recently introduced one are compared with the actual population growth rates in some countries.
Keywords: Fuzzy inference, modeling, simulation, population growth, defuzzification.
Received October 31, 2003; accepted February 24, 2004