The indicator system is the foundation and emphasis in the effectiveness evaluation of system of systems(SoS). In the past, indicator systems were founded based on qualitative methods, and every indicator was mainly d...The indicator system is the foundation and emphasis in the effectiveness evaluation of system of systems(SoS). In the past, indicator systems were founded based on qualitative methods, and every indicator was mainly determined by the expert with experience. This paper proposed a brand-new method to construct indicator systems based on the repeated simulation of the scenario space, and calculated by quantitative data. Firstly, the selection of key indicators using the Gini indicator importance measure(IIM)is calculated by random forests(RFs). Then, principal component analysis(PCA) is applied when we use the selected indicators to construct the composite indicator system of SoS. Furthermore,a set of rulesare is developed to verify the practicability of the indicator system such as correlation, robustness, accuracy and convergence. Experiment shows that the algorithm achieves good results for the construction of composite indicators of So S.展开更多
In the field of weapon system of systems (WSOS) simulation, various indicators are widely used to describe the capability of WSOS, but it is always difficult to describe the comprehensive capability of WSOS quickly an...In the field of weapon system of systems (WSOS) simulation, various indicators are widely used to describe the capability of WSOS, but it is always difficult to describe the comprehensive capability of WSOS quickly and intuitively by visualization of multi-dimensional indicators. A method of machine learning and visualization is proposed, which can display and analyze the capabilities of different WSOS in a two-dimensional plane. The analysis and comparison of the comprehensive capability of different components of WSOS is realized by the method, which consists of six parts: multiple simulations, key indicators mining, three spatial distance calculation, fusion project calculation, calculation of individual capability density, and calculation of multiple capability ranges overlay. Binding a simulation experiment, the collaborative analysis of six indicators and 100 possible kinds of red WSOS are achieved. The experimental results show that this method can effectively improve the quality and speed of capabilities analysis, reveal a large number of potential information, and provide a visual support for the qualitative and quantitative analysis model.展开更多
基金supported by the Major Program of the National Natural Science Foundation of China(U1435218)National Natural Science Foundation of China(6140340171401168)
文摘The indicator system is the foundation and emphasis in the effectiveness evaluation of system of systems(SoS). In the past, indicator systems were founded based on qualitative methods, and every indicator was mainly determined by the expert with experience. This paper proposed a brand-new method to construct indicator systems based on the repeated simulation of the scenario space, and calculated by quantitative data. Firstly, the selection of key indicators using the Gini indicator importance measure(IIM)is calculated by random forests(RFs). Then, principal component analysis(PCA) is applied when we use the selected indicators to construct the composite indicator system of SoS. Furthermore,a set of rulesare is developed to verify the practicability of the indicator system such as correlation, robustness, accuracy and convergence. Experiment shows that the algorithm achieves good results for the construction of composite indicators of So S.
基金supported by the National Natural Science Foundation of China(U14352186140340161273189)
文摘In the field of weapon system of systems (WSOS) simulation, various indicators are widely used to describe the capability of WSOS, but it is always difficult to describe the comprehensive capability of WSOS quickly and intuitively by visualization of multi-dimensional indicators. A method of machine learning and visualization is proposed, which can display and analyze the capabilities of different WSOS in a two-dimensional plane. The analysis and comparison of the comprehensive capability of different components of WSOS is realized by the method, which consists of six parts: multiple simulations, key indicators mining, three spatial distance calculation, fusion project calculation, calculation of individual capability density, and calculation of multiple capability ranges overlay. Binding a simulation experiment, the collaborative analysis of six indicators and 100 possible kinds of red WSOS are achieved. The experimental results show that this method can effectively improve the quality and speed of capabilities analysis, reveal a large number of potential information, and provide a visual support for the qualitative and quantitative analysis model.
