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面向机器人的多种单一运动灵活性指标,基于主成分分析(principal component analysis,PCA)方法对其综合运动灵活性进行分析和评价,评价结果受奇异位形处指标极端值的影响。进而将模糊集引入PCA,使用模糊主成分分析(fuzzy principal component analysis,FPCA)方法进行机器人灵活性综合评价。通过调节模糊隶属函数,吸收奇异位形及其附近的极端值,突出主要信息,提高分析结果的准确性和可信度,提高机器人灵活性的综合评价的效果,从而提出一种基于多种性能指标的含奇异位形的机器人任务优选的新方法。
Based on the principal component analysis (PCA) method, this paper analyzes and evaluates the comprehensive flexibility of the robot. The results of the evaluation are affected by the extreme value of the index of the singularity. Then the fuzzy set is introduced into PCA, and the fuzzy principal component analysis (FPCA) method is used to evaluate the flexibility of the robot. By adjusting the fuzzy membership function, absorbing the extreme value of singularity and its vicinity, highlighting the main information, improving the accuracy and reliability of the analysis results and improving the comprehensive evaluation of the flexibility of the robot, A Novel Method for Optimizing Mission Indicators with Singularities.