含噪声的转子碰摩混沌信号分类识别

采用基于竞争学习和聚类分析的学习向量量化 ( LVQ)方法 ,研究转子碰摩混沌响应信号的神经网络分类识别问题 ,给出了相应的理论分析和计算结果 ,着重研究了 LVQ网络在不同噪声时的识别情况。分析结果表明 ,该方法可以实现转子碰摩混沌信号与其它响应信号的分类识别 ,并且具有良好的抗噪性能 ,为转子碰摩混沌信号的分类识别提供了一种较为直接的实时处理方法。

Classification and Identification of the Rotor Rub-Impacting Chaotic Signals with Noise

In this paper,we will try to identify the chaotic signals of the rub-impacting rotor system using learning vector quantization (LVQ) which is based on competitive learning and clustering analysis.The theory analysis and results of the computation are given.We have mainly studied the situations of the different classification when the response signals are in the different ratio of signal to noise.Our research shows that the LVQ neural network not only can identify rub-impacting chaotic signals but also work well for the signals with noise.So the network supplies a direct method for classifying such nonlinear signals of the rub-impacting rotor system and a possibility of practical application for a real rotor system.