模糊特征目标的相对熵识别法

Relative entropy method in target recognition with fuzzy features

针对模糊特征的目标识别问题,提出了一种结合模糊建模和改进CRITIC方法的相对熵识别方法。计算多个时刻观测值的统计特征,通过模糊建模将观测值转化为模糊数;基于模糊数距离测度,定义并计算目标特征值和观测值之间的相似度;对CRITIC方法进行改进,提出一种目标特征客观权重的求解方法;根据相似度和特征权重,使用相对熵排序法得到识别结果。仿真结果显示:模糊特征能够更好地体现识别中的不确定性,所提方法对模糊特征的目标识别率高,实时性和鲁棒性好,具有一定的应用价值。

A relative entropy method combining fuzzy modeling and improved CRITIC was presented to recognize targets with fuzzy features.The observed values from multiple times were converted into fuzzy numbers through fuzzy modeling based on the statistical characteristics of multiple sets of the observed values.As a result of measuring the distance between the fuzzy numbers,similarities between the values of the target feature and the observed values were determined.The improved CRITIC was proposed to calculate the objective weights of the target features.According to the feature weights and the similarities between the target feature values and the observed values,the recognition result was obtained by the relative entropy evaluation method.The simulation results indicate that the uncertainty in target recognition is better reflected by the fuzzy features,and the proposed method has a high target recognition rate for the target with fuzzy features with good real-time and robustness,which has a certain application value.