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在矩阵代数，数值计算，数字信号处理，振动理论，神经网络控制动态仿真领域都有广泛的应用。-In the matrix algebra, numerical computing, digital signal processing, vibration theory, the neural network to control the field of dynamic simulation has a wide range of applications.
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C-均值聚类
BP神经网络信号分析与处理方法，对模拟产生的数据样本进行训-C-means clustering analysis and BP neural network signal processing method, the simulated data generated training samples
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neural network coding for signal classifications
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基于决策论方法识别ask,fsk,psk信号，用神经网络找出判决门限-Decision theory-based methods to identify ask, fsk, psk signal neural network to identify the decision threshold
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BP神经网络的数据分类-语音特征信号分类-bp Neural network data classification- voice feature signal classification
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bp神经网络实现数据分类——基于语音信号的特征分类-bp neural network data classification- classification based on the characteristics of the speech signal
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BP神经网络的数据分类-语音特征信号分类-BP neural network data classification- voice feature signal classification
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BP neural network for classification of data-voice signal classification
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BP神经网络的数据分类-语音特征信号分类-BP neural network data classification- speech feature signal classification
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案例神经网络的数据分类-语音特征信号分类-Case neural network data classification- speech feature signal classification
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语音信号识别 BP神经网络的研究 只需要简答的修改就可以了-Speech signal recognition BP neural network research
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语音信号识别 BP神经网络的研究 只需要简答的修改就可以饿了-Speech signal recognition BP neural network research
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主要是利用BP神经网络来进行语音信号的分析，提取语音信号的MFCC参数进行分类-Mainly using BP neural network to carry voice signals analysis, MFCC parameter extraction of speech signal classification
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BP神经网络的数据分类-语音特征信号分类-BP neural network data classification- voice feature signal classification
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基于神经网络的模拟调制识别，首先是模拟信号的产生，再对所获的信号进行训练，最后进行识别-Analog modulation recognition based on neural network, the first is the generation of analog signal, then to training of received signal, finally for identification
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Bp神经网络实现语音信号的分类 效果十分好 值得参考-Language signal classification Bp neural network
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BP神经网络的数据分类——————语音特征信号分类-Data BP neural network classification speech characteristic signal classification
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BP神经网络的数据分类-语音特征信号分类-BP neural network data classification- the speech characteristic signal classification
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把HHT和神经网络结合起来，识别出是否故障。对于HHT变换，这里用到的是它的IMF分解，然后用能量理论来判别哪些模组是虚假分量，哪些是是真实分量。对于EMD分解后的谱进行特征提取 ，利用的理论基础就是模糊熵。计算出真实 分量的模糊熵，作为输入层；输出层为两个神经元，为0（故障 ）1（正常 （正常 ）判别该信号是否出现故障。-The combination of HHT and neural network to identify whether the fault. For the HHT tr
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The commonly used digital signal modulation based on artificial neural network, PV modules contain, MPPT module, BOOST module, inverter module, Chaos indicator for Lyapunov index calculation.
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