An Intelligent Algorithm Based on Grid Searching and Cross Validation and its Application in Population Analysis

Abstract

Population statistic and forecast is important basis that government establishes correlative policy, population’s all characteristic has strong non-linear speciality because of all kinds of effects. A cross validation optimized parameter least support vector machine method of population statistic and forecast is presented aiming at bad precision and lack of rationality of all approximate model at present. Complicated and strong nonlinear population characteristic relation is simulated by network design and conformation of the least square support vector machine learning algorithm and selecting the optimized support vector machine parameters by the method of grid searching and cross validation. The model is HverifiedH by taking population growth rate HforH example, cross validation optimized parameter least support vector machine algorithm has strong ability of nonlinear mapping and self-learning, it avoids availably phenomenon of partial minimum and overfitting, the future population problem can be accurately calculated and judged , it gains high precision by comparing numerical value of network output with fitting value and numerical real value. It provides a new artificial intelligent approach for population analysis.