DocumentCode :
724085
Title :
Chebyshev-polynomial neuronet, WASD algorithm and world population prediction from past 10000-year rough data
Author :
Dongsheng Guo ; Yunong Zhang ; Liangyu He ; Keke Zhai ; Hongzhou Tan
Author_Institution :
Sch. of Inf. Sci. & Technol., Sun Yat-sen Univ. (SYSU), Guangzhou, China
fYear :
2015
fDate :
23-25 May 2015
Firstpage :
1702
Lastpage :
1707
Abstract :
The population of the world attracts considerable attention, as it is closely related to the development of human society. The prediction of the world population, which can be used for planning and research, is becoming more and more important. In this report, we present a neuronet approach for world population prediction. Note that the history population data contain the general regularity of the population development, and are also the comprehensive reflection of the population development under the influence of all factors (e.g., natural environment, policy and economy). Thus, using the past 10000-year rough data, a 3-layer feedforward neuronet equipped with the weights-and-structure-determination (WASD) algorithm is constructed for the prediction of the world population in this report. Via various numerical testings, such a WASD neuronet indicates that there are several possibilities of the change of the world population in the future. With the most possibility, the trend of the world population in the next decade is: to rise, peak in 2020, and then decline.
Keywords :
Chebyshev approximation; demography; feedforward neural nets; planning; polynomials; research and development; 3-layer feedforward neuronet; Chebyshev-polynomial neuronet; WASD algorithm; human society; numerical testings; planning; research; weights-and-structure-determination algorithm; world population prediction; Chebyshev approximation; Neurons; Prediction algorithms; Sociology; Statistics; Training; Approximation; Chebyshev-Polynomial Neuronet; Prediction; WASD Algorithm; World Population;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (CCDC), 2015 27th Chinese
Conference_Location :
Qingdao
Print_ISBN :
978-1-4799-7016-2
Type :
conf
DOI :
10.1109/CCDC.2015.7162194
Filename :
7162194
Link To Document :
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