The classification technique uses various features of the signal amplitude, frequency, and power spectrum applied to the fuzzy classifier.
本文研究了一种基于模糊分类的调制信号识别方法，即提取信号时域、频域、功率谱等统计特性，利用模糊分类器进行分类识别。
Training the multi-fault classifier only needs a small quantity of fault data samples in time domain, and does not need signal preprocessing for extracting signal features.
应用结果表明，不必进行信号预处理以提取特征量，只需要用少量的时域故障数据样本建立故障分类器。
The forth-order and sixth-order cumulants of received signal are adopted for features extraction while RBF neural networks with a new designed training cost function being used for classifier.
采用信号四阶和六阶统计量提取信号特征，使用新设计的误差函数训练RBF神经网络，使得识别的效率和正确度得到了明显的改善。
应用推荐