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Seismic comprehensive forecast based on modified project pursuit regression 被引量:3
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作者 Anxu Wu Xiangdong Lin +4 位作者 Changsheng Jiang Yongxian Zhang Xiaodong Zhang Mingxiao Li Ping'an Li 《Earthquake Science》 CSCD 2009年第5期563-574,共12页
In the research of projection pursuit for seismic comprehensive forecast, the algorithm of projection pursuit regression (PPR) is one of most applicable methods. But generally, the algorithm structure of the PPR is ... In the research of projection pursuit for seismic comprehensive forecast, the algorithm of projection pursuit regression (PPR) is one of most applicable methods. But generally, the algorithm structure of the PPR is very complicated. By partial smooth regressions for many times, it has a large amount of calculation and complicated extrapolation, so it is easily trapped in partial solution. On the basis of the algorithm features of the PPR method, some solutions are given as below to aim at some shortcomings in the PPR calculation: to optimize project direction by using particle swarm optimization instead of Gauss-Newton algorithm, to simplify the optimal process with fitting ridge function by using Hermitian polynomial instead of piecewise linear regression. The overall optimal ridge function can be obtained without grouping the parameter optimization. The modeling capability and calculating accuracy of projection pursuit method are tested by means of numerical emulation technique on the basis of particle swarm optimization and Hermitian polynomial, and then applied to the seismic comprehensive forecasting models of poly-dimensional seismic time series and general disorder seismic samples. The calculation and analysis show that the projection pursuit model in this paper is characterized by simplicity, celerity and effectiveness. And this model is approved to have satisfactory effects in the real seismic comprehensive forecasting, which can be regarded as a comprehensive analysis method in seismic comprehensive forecast. 展开更多
关键词 particle swarm optimization Hermitian polynomial projection pursuit numerical modeling forecasting regression model
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Study on Population Forecast Model in Planning of Land Use
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作者 ZHANG Yan-fen LI Ying-chao CHEN Wei-qiang 《Asian Agricultural Research》 2011年第4期63-65,69,共4页
On the basis of describing characteristics and condition of application of natural growth model of population,weighted average growth model,regression forecast model and GM(1,1)forecast model,taking Gushi County in He... On the basis of describing characteristics and condition of application of natural growth model of population,weighted average growth model,regression forecast model and GM(1,1)forecast model,taking Gushi County in Henan Province as an example,according to the statistics of population in Gushi County Statistical Yearbook from 1991 to 2007,we establish four models to conduct fitting on population change respectively,and meanwhile,we predict population size from 2008 to 2009 and conduct preciseness test on the population size.The test results show that the preciseness of forecast results of natural growth model is not high,and the preciseness of forecast results of weighted average growth model is not scientific when the total size of population is unstable.The results of GM(1,1)forecast model and regression forecast model largely conform to the actual data,so we can take the mean of the two as the final forecast result. 展开更多
关键词 Planning of land use Population forecast model regression forecast model GM(1 1)gray forest model China
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