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。
On the design of classifier, by introducing support vector machine, Chinese tone recognition rate is improved under low signal noise rate conditions.
在分类器设计方面，通过引入支持矢量机，进一步提高低信噪比下的汉语声调识别率。
In the process of test, principal component analysis is used as data preprocessing to extract the feature index from vibration signal statistic features as the input of SVDD classifier.
在测试的过程中，主成分分析作为数据预处理，提取特征指数从振动信号的统计特征作为输入SVDD分类。
In the process of test, principal component analysis is used as data preprocessing to extract the feature index from vibration signal statistic features as the input of SVDD classifier.
在测试的过程中，主成分分析作为数据预处理，提取特征指数从振动信号的统计特征作为输入SVDD分类。
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