华中科技大学学报（自然科学版）2023，Vol.51Issue(12)：1-7,7.DOI:10.13245/j.hust.238733
面向动态优化的不规则区域神经网络构建方法
Construction method of irregular regional neural network for dynamic optimization
摘要
Abstract
To efficiently solve the dynamic optimization problem with implicit state equation using the surrogate model-based method,a space partition strategy based on Gaussian mixture model clustering algorithm was proposed to construct the neural network model of the implicit state equation.The state space was divided into several irregular local regions according to the nonlinearity of the right-hand-side functions in the state equation,the global neural network model of the state equation was generated by combining the neural network models of the local regions,then the dynamic optimization problem was solved based on the global neural network model.In the two engineering examples,the errors between the approximate optimal objective function values based on the proposed method and exact optimal objective function values are 0.013 4％and 0.003 0％,respectively,which show that the proposed method guarantees the solution accuracy of dynamic optimization problem based on the surrogate model-based method.关键词
动态优化/空间划分/不规则区域/高斯混合模型聚类/神经网络模型Key words
dynamic optimization/space partitioning/irregular region/Gaussian mixture model clustering/neural network model分类
信息技术与安全科学引用本文复制引用
张琪,吴义忠,邢涛,乔平..面向动态优化的不规则区域神经网络构建方法[J].华中科技大学学报（自然科学版）,2023,51(12):1-7,7.基金项目
国家重点研发项目(2018YFB1700905). （2018YFB1700905）
