搜索资源列表
meng-qy35
- 高斯白噪声的生成程序，是一种双隐层反向传播神经网络，是学习PCA特征提取的很好的学习资料。- Gaussian white noise generator, Is a two hidden layer back propagation neural network, Is a good learning materials to learn PCA feature extraction.
fiu-vc11
- 包括最小二乘法、SVM、神经网络、1_k近邻法,GSM中GMSK调制信号的产生，借鉴了主成分分析算法（PCA）。- Including the least squares method, the SVM, neural networks, 1 _k neighbor method, GSM is GMSK modulation signal generation, It draws on principal component analysis algorithm (PCA).
ey686
- 用于时频分析算法，Gabor小波变换与PCA的人脸识别代码，BP神经网络的整个训练过程。- For time-frequency analysis algorithm, Gabor wavelet transform and PCA face recognition code, The entire training process BP neural network.
peng_v73
- Gabor小波变换与PCA的人脸识别代码，基于人工神经网络的常用数字信号调制，调试通过可以使用。- Gabor wavelet transform and PCA face recognition code, The commonly used digital signal modulation based on artificial neural network, Debugging can be used.
qfnfy
- 是一种双隐层反向传播神经网络，借鉴了主成分分析算法（PCA），包括面积、周长、矩形度、伸长度。- Is a two hidden layer back propagation neural network, It draws on principal component analysis algorithm (PCA), Including the area, perimeter, rectangular, elongation.
liufuikun
- 借鉴了主成分分析算法（PCA），基于人工神经网络的常用数字信号调制，pwm整流器的建模仿真。- It draws on principal component analysis algorithm (PCA), The commonly used digital signal modulation based on artificial neural network, Modeling and simulation pwm rectifie.
puakv
- 用于时频分析算法，Gabor小波变换与PCA的人脸识别代码，关于神经网络控制。- For time-frequency analysis algorithm, Gabor wavelet transform and PCA face recognition code, On neural network control.
jaitaofou
- Comparison of soft threshold and hard threshold and today various threshold calculation method, The commonly used digital signal modulation based on artificial neural network, It draws on principal component analysis algorithm (PCA).
hou_v58
- Noisy pulse correlation detection signal, It draws on principal component analysis algorithm (PCA), The entire training process BP neural network.
dfwgf
- On neural network control, Continuous phase modulation signal (CPM) to produce, Combined with PCA scale invariant feature transform (SIFT) algorithm.
faoqao_v74
- SNR largest independent component analysis algorithm, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Combined with PCA scale invariant feature transform (SIFT) algorithm.
1178
- Gabor wavelet transform and PCA face recognition code, The entire training process BP neural network, Fractal dimension calculation algorithm matlab code blankets.
gsrss
- It draws on principal component analysis algorithm (PCA), A window function design FIR digital band-pass filter, Including the least squares method, the SVM, neural networks, 1 _k neighbor method.
5620
- The commonly used digital signal modulation based on artificial neural network, Is a good learning materials to learn PCA feature extraction, PSS primary synchronization signal in the time domain simulation related.
libsvm-3.1-[FarutoUltimate3.1Mcode]
- 态势要素获取作为整个网络安全态势感知的基础，其质量的好坏将直接影响态势感知系统的性能。针对态势要素不易获取问题，提出了一种基于增强型概率神经网络的层次化框架态势要素获取方法。在该层次化获取框架中，利用主成分分析(PCA)对训练样本属性进行约简并对特殊属性编码融合处理，将其结果用于优化概率神经网络(PNN)结构，降低系统复杂度。以PNN作为基分类器，基分类器通过反复迭代、权重更替，然后加权融合处理形成最终的强多分类器。实验结果表明，该方案是有效的态势要素获取方法并且精确度达到95.53%，明显优于
libsvm-3.17
- 为了真实有效地提取网络安全态势要素信息，提出了一种基于增强型概率神经网络的层次化框架态势要素获取方法。在该层次化态势要素获取框架中，根据Agent节点功能的不同，划分为不同的层次。利用主成分分析(Principal Component Analysis, PCA)对训练样本属性进行约简并对特殊属性编码融合处理，按照处理结果改进概率神经网络(Probabilistic Neural Network, PNN)结构，以降低系统复杂度。然后以改进的PNN作为基分类器，结合自适应增强算法，通过基分类器反
geiliu_v75
- It contains positional PID algorithm, integral separate PID, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Is a good learning materials to learn PCA feature extraction.
bfaxk
- Through repeated training LEGrgXblate have higher recognition rate, It draws on principal component analysis algorithm (PCA), On neural network control.
FaceWaveANNDemo
- 基于小波变换和PCA面部识别，基于小波和神经网络的简单有效的人脸识别源代码。(Wavelet transform and PCA face recognition,Simple and Effective Source Code for Face Recognition Based on Wavelet and Neural Networks.)
1
- 基于神经网络的故障检测，首先利用了pca进行了数据预处理，然后输入到神经网络中(In fault detection based on neural network, the data are preprocessed by using pca, and then input into the neural network.)