This paper is mainly concerned with extracting effective features from the recognized or classified signals by selecting wavelet packet basis via given training sample sets.
本文主要研究由给定的训练样本集，如何选择最优小波包基，从被识别或分类的信号中提取具有最大可分性的特征。
In the study of brain-computer interfaces, a method based on best basis of wavelet packet decomposition was proposed. The method is used for the feature extraction of electroencephalogram.
在脑机接口研究中，针对脑电特征抽取，提出一种基于小波包最优基分解的方法。
This paper briefly expounded the basic theory of wavelets and wavelet packet, and on the basis of using wavelet packet, introduced the general principles in vibration signal denoise.
简要阐述了小波分析、小波包分析的基本原理，并在此基础上介绍了利用小波包给信号去噪的一般原理。
On the basis of wavelet packet decomposition technique introduced firstly, the measured unstable oil-film oscillations of single-disc and single-span rotor system were investigated.
在介绍小波包分解原理的基础上，对试验测得的单盘单跨转子系统的油膜振荡非平稳信号用小波包分解方法进行了研究。
The L F signals are transformed by entropy based on wavelet packet algorithms for best basis selection, and the results are displayed in time- frequency space namely phase plane.
该方法采用基于熵的小波包最佳基选取准则，对局部损伤信号进行自适应小波包分解，将分解结果显示于时—频空间即相平面上。
A novel pattern recognition method based on wavelet packet analysis and radial basis function network is presented in this paper.
论文给出了改进型径向基网络应用示例，验证了改进型径向基网络的函数实现功能和模式分类功能。
A novel pattern recognition method based on wavelet packet analysis and radial basis function network is presented in this paper.
论文给出了改进型径向基网络应用示例，验证了改进型径向基网络的函数实现功能和模式分类功能。
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