摘要
给出了一种利用演化计算方法求解微分方程中的参数识别类型反问题的方法。该方法把参数识别问题转化为泛函的优化问题用演化算法来求解 ,指定待定参数的函数类形式 ,用遗传算法 (GeneticAlgorithms)来演化待求参数的最优估计值 ,并将该方法运用于线性扩散方程和拟线性对流扩散反方程反问题的数值模拟中。
A general approach based on evolutionary algorithms to inverse parameter identification problems of PDEs is introduced. The evolutionary algorithms is used for solving the inverse problem as an optimization problem. This approach can evolve the optimal coefficient estimation by genetic algorithms (GA) in the giving function space of unknown parameter from observation data objectively and automatically. The approach has also been applied to parameter identification problem of linear and quasi linear parabolic equations. The numerical results demonstrated the effectiveness of this method.
出处
《计算物理》
CSCD
北大核心
2000年第5期511-517,共7页
Chinese Journal of Computational Physics
基金
国家自然科学基金
武汉市晨光计划资助项目
关键词
反问题
参数识别
演化计算
遗传算法
抛物型方程
inverse problem
parameter identification
evolutionary algorithms
genetic algorithms
