大气科学学报2011，Vol.34Issue(5)：529-537,9.
遗传算法求解最大预报误差的可行性研究
The feasibility of genetic algorithm for solving maximum prediction errors
摘要
Abstract
Estimating the upper bound of maximum prediction errors can be achieved by using the conditional nonlinear optimal perturbation（CNOP） .Generally,CNOP is obtained by the constrained optimization algorithm based on gradient information provided by the associated adjoint model.However,when the nonlinear model contains discontinuous ＂on-off＂ switches,the conventional adjoint method cannot provide the correct gradient direction,and eventually induces the optimization to fail.Hence,genetic algorithm（GA） with self-adaptive mutation and blend crossover operators,tournament selection mechanism and niche strategy are adopted to solve the upper bound of maximum prediction errors in this paper.In order to test the effectiveness of the new method,we take the modified Lorenz model as the prediction model,and make comparison between the new method and the conventional adjoint method respectively aiming to three different initial reference states.The numerical experiment results show that the new method is more effective for solving the upper bound of maximum prediction errors.关键词
“on-off”开关/伴随方法/遗传算法/最大预报误差上界Key words
＂on-off＂ switch/adjoint method/genetic algorithm（GA）/the upper bound of maximum prediction error分类
天文与地球科学引用本文复制引用
郑琴,张露,戴毅,陆小庆..遗传算法求解最大预报误差的可行性研究[J].大气科学学报,2011,34(5):529-537,9.基金项目
国家自然科学基金资助项目（40975063 （）
40830955） （）
