Control-Relevant System Identification By Using Wavelet Analysis

使用小波分析进行控制相关系统识别

基本信息

批准号：
14550441
负责人：
NAKANO Kazushi
金额：
$ 1.73万
依托单位：
The University of Electro-Communications
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2002
资助国家：
日本
起止时间：
2002 至 2004
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-14550441/
关键词：
Wavelet System Identification Control Systems Dead Time Transfer Function Gabor function Power Amplifier Blind Source Separation 連続ウェーブレット変換時変システム伝達関数推定むだ時間システム

项目摘要

1.We studied a condition for getting the estimate of transfer function from the ratio of wavelet spectra of input/output data.2.In connection with 1, we proposed an "indirect method" based on wavelet spectra and a "direct method" based on wavelet transform of input/output data, and compared two methods. Consequently, we showed that desired filter characteristics (e.g.LPF) are realized with some degrees of freedom.3.In connection with 2, we showed that when adopting a complex wavelet such as Gabor wavelet, the bias of estimate of spectrum is suppressed.4.Through the identification of control systems of a thermal power plant and the identification of control systems for Nox decomposition, we verified the feasibility and effectiveness of our approach.5.For the case that there is a stable transfer function with dead time in the forward loop and also there is a feedback loop, we considered a dead-time estimation problem. Consequently, we were able to prove that the previous result is extensible to this practical case.6.We showed the relation between tire sounds and tread patterns by using the wavelet-based correlation analysis.7.Applying the wavelet transform to the blind separation of sound source for a convolutive mixture case, we made the previous methods applicable to the convolutive mixture case by transforming time-frequency dependent signals into temporal signals by fixing the frequency

1.我们研究了一种条件，以从输入/输出数据的小波光谱之比中获取传输函数的估计值。2。在与1的连接中，我们提出了一种基于小波光谱的“间接方法”和基于“直接方法”的“间接方法”在输入/输出数据的小波转换上，并比较了两种方法。因此，我们表明所需的滤波器特性（例如LPF）以某种自由度实现。3.在与2的联系中，我们表明，当采用复杂小波（例如Gabor小波）时，抑制了光谱的估计值。4。通过识别热电厂的控制系统和NOX分解的控制系统的识别，我们验证了我们方法的可行性和有效性。循环，还有一个反馈循环，我们考虑了一个死时间估计问题。因此，我们能够证明以前的结果可扩展到这种实际情况。6。我们通过使用基于小波的相关性分析显示了轮胎声音和胎面模式之间的关系。7。将小波转换为盲目的分离，备量混合物案例的声源，我们通过固定频率将频率依赖性信号转换为时间信号，使以前的方法适用于备量混合物盒