针对我国当前经济、政策变动的大背景,提出了采用数据分组处理方法GMDH(group method of data handling)结合多结构突变理论,实现时序突变点自动搜索建模,建立了中长期负荷预测的GMDH多结构自动搜索模型。该模型能够客观准确地搜索时间...针对我国当前经济、政策变动的大背景,提出了采用数据分组处理方法GMDH(group method of data handling)结合多结构突变理论,实现时序突变点自动搜索建模,建立了中长期负荷预测的GMDH多结构自动搜索模型。该模型能够客观准确地搜索时间序列中的所有突变点,并充分利用突变点信息修正由于经济环境和突发事件引起的预测偏差,大大提高了传统时序外推预测模型的精度。华东地区的实际算例结果表明了该模型的有效性。展开更多
In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Mo...In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Modified non-dominated sorting genetic algorithm II(NSGA II) was used for multi-objective optimization of automotive S-rail considering absorbed energy(E), peak crushing force(Fmax) and mass of the structure(W) as three conflicting objective functions. In the multi-objective optimization problem(MOP), E and Fmax are defined by polynomial models extracted using the software GEvo M based on train and test data obtained from numerical simulation of quasi-static crushing of the S-rail using ABAQUS. Finally, the nearest to ideal point(NIP)method and technique for ordering preferences by similarity to ideal solution(TOPSIS) method are used to find the some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. Results represent that the optimum design point obtained from TOPSIS method exhibits better trade-off in comparison with that of optimum design point obtained from NIP method.展开更多
文摘针对我国当前经济、政策变动的大背景,提出了采用数据分组处理方法GMDH(group method of data handling)结合多结构突变理论,实现时序突变点自动搜索建模,建立了中长期负荷预测的GMDH多结构自动搜索模型。该模型能够客观准确地搜索时间序列中的所有突变点,并充分利用突变点信息修正由于经济环境和突发事件引起的预测偏差,大大提高了传统时序外推预测模型的精度。华东地区的实际算例结果表明了该模型的有效性。
文摘In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Modified non-dominated sorting genetic algorithm II(NSGA II) was used for multi-objective optimization of automotive S-rail considering absorbed energy(E), peak crushing force(Fmax) and mass of the structure(W) as three conflicting objective functions. In the multi-objective optimization problem(MOP), E and Fmax are defined by polynomial models extracted using the software GEvo M based on train and test data obtained from numerical simulation of quasi-static crushing of the S-rail using ABAQUS. Finally, the nearest to ideal point(NIP)method and technique for ordering preferences by similarity to ideal solution(TOPSIS) method are used to find the some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. Results represent that the optimum design point obtained from TOPSIS method exhibits better trade-off in comparison with that of optimum design point obtained from NIP method.
