体系效能评估指标数量多、维数高,指标之间关联,且具有协同效应,加大了效能评估的计算复杂性。针对这一问题,建立考虑协同效应的联合作战体系效能指标灰色主成分分析(grey principal component analysis,GPCA)重构模型。首先,分析联合...体系效能评估指标数量多、维数高,指标之间关联,且具有协同效应,加大了效能评估的计算复杂性。针对这一问题,建立考虑协同效应的联合作战体系效能指标灰色主成分分析(grey principal component analysis,GPCA)重构模型。首先,分析联合防空作战体系的作战使命、任务、流程,构建其效能评估指标体系,并运用灰色关联模型分析指标间是否存在协同效应。其次,基于指标间存在的协同效应,给出3种重构效能评估指标体系的策略,并结合GPCA方法,构建具有协同效应的GPCA模型,对评估指标体系进行降维。最后,将所提方法应用于联合防空作战体系效能评估案例,筛选出具有协同效应的指标,重构效能评估指标体系。计算结果与方法对比分析表明,所提方法能够有效发现指标间的协同效应,重构后的评估指标体系保持了“同构性”。展开更多
Based on principal component analysis, a multiple neural network was proposed. The principal component analysis was firstly used to reorganize the input variables and eliminate the correlativity. Then the reorganized ...Based on principal component analysis, a multiple neural network was proposed. The principal component analysis was firstly used to reorganize the input variables and eliminate the correlativity. Then the reorganized variables were divided into 2 groups according to the original information and 2 corresponding neural networks were established. A radial basis function network was used to depict the relationship between the output variables and the first group input variables which contain main original information. An other single-layer neural network model was used to compensate the error between the output of radial basis function network and the actual output variables. At last, The multiple network was used as soft sensor for the ratio of soda to aluminate in the process of high-pressure digestion of alumina. Simulation of industry application data shows that the prediction error of the model is less than 3%, and the model has good generalization ability.展开更多
文摘体系效能评估指标数量多、维数高,指标之间关联,且具有协同效应,加大了效能评估的计算复杂性。针对这一问题,建立考虑协同效应的联合作战体系效能指标灰色主成分分析(grey principal component analysis,GPCA)重构模型。首先,分析联合防空作战体系的作战使命、任务、流程,构建其效能评估指标体系,并运用灰色关联模型分析指标间是否存在协同效应。其次,基于指标间存在的协同效应,给出3种重构效能评估指标体系的策略,并结合GPCA方法,构建具有协同效应的GPCA模型,对评估指标体系进行降维。最后,将所提方法应用于联合防空作战体系效能评估案例,筛选出具有协同效应的指标,重构效能评估指标体系。计算结果与方法对比分析表明,所提方法能够有效发现指标间的协同效应,重构后的评估指标体系保持了“同构性”。
基金Project ( 2001AA411040 ) supported by the National High Technology Development Program of China project(2002CB312200) supported by the National Fundamental Research and Development Program of China
文摘Based on principal component analysis, a multiple neural network was proposed. The principal component analysis was firstly used to reorganize the input variables and eliminate the correlativity. Then the reorganized variables were divided into 2 groups according to the original information and 2 corresponding neural networks were established. A radial basis function network was used to depict the relationship between the output variables and the first group input variables which contain main original information. An other single-layer neural network model was used to compensate the error between the output of radial basis function network and the actual output variables. At last, The multiple network was used as soft sensor for the ratio of soda to aluminate in the process of high-pressure digestion of alumina. Simulation of industry application data shows that the prediction error of the model is less than 3%, and the model has good generalization ability.
