信号多维特征向量的评价模型

Multidimensional Feature Vector Evaluation Model for Signals

采用特征值标准差和欧氏距离分布熵方法,构造信号多维特征向量评价模型,探讨信号特征向量稳定性和可分性的评价标准.仿真实验结果表明,多维特征向量在稳定性和可分性指标上达到0.192 3和0.287 4,相比一维特征向量有较大的优势,可以作为应力波信号特征向量的评价方法,能够为后续智能检测选取出性能较好的特征向量.

Using the feature standard deviation and Euclidean distance distribution entropy methods,construct a signal multidimensional eigenvector evaluation model to explore the evaluation standard of signal feature stability and separability.Simulation results show that the multi-dimensional feature vectors reach 0.1923 and 0.2874 in terms of stability and separability,compared with the one-dimensional feature vector,the multi-dimensional feature vector has a great advantage,so the evaluation methods can be used as a stress wave signal feature vector evaluation methods for subsequent intelligent detection selects better feature vectors.