Name
Abstract | ||
|---|---|---|
In this paper, improved Logistic models are given, which are called Logarithm Logistic Models. Based on U.S. Census data, the parameters of the models were estimated by applying the Least Squares Method. The experiments show that the prediction value of the new models is much closer to the actual value than the classical Logistic model. Finally, through analyzing the rationality of the maximum population capacity, the trend of Logistic curves and the rationality of prediction value, the most appropriate model is recommended. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CIS.2012.30 | CIS |
Keywords | Field | DocType |
growth rate,logistic curve,u.s. census data,maximum population capacity,population prediction,improved logarithm logistic models,new model,squares method,classical logistic model,logarithm logistic model,prediction value rationality,demography,least squares approximations,least squares method,appropriate model,logistic curves,improved logistic model,prediction theory,logarithm logistic models,actual value,prediction value,logistic model | Econometrics,Population,Logistic model tree,Artificial intelligence,Logarithm,Logistic regression,Logit,Multinomial logistic regression,Generalised logistic function,Statistics,Logistic function,Mathematics,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4673-4725-9 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Li | 1 | 924 | 94.55 |
| Tianfei Wang | 2 | 4 | 2.89 |
| Li-Ping Jia | 3 | 54 | 7.81 |